Harinder Singh Bawa            

                                   MSc. (Hons) Physics, Ph.D


Department of Physics ,                                                       =         

2345 E. San Ramon Avenue,                                                          

Mail Stop MH37,                                                                        

California State University,


Phone:- (559)278-2810


 My Current and Past Research Experiments:


Science in Elementary School

  • Grade6_Science> href="http://www.onlinemathlearning.com/common-core-math-worksheets-grade6.html>Grade6_Maths>

Scientific Computing Activities

Useful links for Scientific Computing Activities

Scientific Computing Presentations & Documentations

Condor Troubleshooting

Instructions for CalcHep Generation

Lectures to CSU Nuclear and Particle Physics Consortium(CSUNUPAC) students

Common Mchine Learning Algorithms

We are probably living in the most defining period of human history. The period when computing moved from large mainframes to PCs to cloud. But what makes it defining is not what has happened, but what is coming our way in years to come. What makes this period exciting for some one like me is the democratization of the tools and techniques, which followed the boost in computing. Today, as a data scientist, I can build data crunching machines with complex algorithms for a few dollors per hour. But, reaching here wasn¿t easy! I had my dark days and nights. Broadly, there are 3 types of Machine Learning Algorithms..

1. Supervised Learning

How it works: This algorithm consist of a target / outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables). Using these set of variables, we generate a function that map inputs to desired outputs. The training process continues until the model achieves a desired level of accuracy on the training data. Examples of Supervised Learning: Regression, Decision Tree, Random Forest, KNN, Logistic Regression etc.

2. Unsupervised Learning

How it works: In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. Examples of Unsupervised Learning: Apriori algorithm, K-means. 3. Reinforcement Learning: How it works: Using this algorithm, the machine is trained to make specific decisions. It works this way: the machine is exposed to an environment where it trains itself continually using trial and error. This machine learns from past experience and tries to capture the best possible knowledge to make accurate business decisions. Example of Reinforcement Learning: Markov Decision Process List of Common Machine Learning Algorithms Here is the list of commonly used machine learning algorithms. These algorithms can be applied to almost any data problem: Linear Regression Logistic Regression Decision Tree SVM Naive Bayes KNN K-Means Random Forest Dimensionality Reduction Algorithms Gradient Boost & Adaboost 1. Linear Regression It is used to estimate real values (cost of houses, number of calls, total sales etc.) based on continuous variable(s). Here, we establish relationship between independent and dependent variables by fitting a best line. This best fit line is known as regression line and represented by a linear equation Y= a *X + b. The best way to understand linear regression is to relive this experience of childhood. Let us say, you ask a child in fifth grade to arrange people in his class by increasing order of weight, without asking them their weights! What do you think the child will do? He / she would likely look (visually analyze) at the height and build of people and arrange them using a combination of these visible parameters. This is linear regression in real life! The child has actually figured out that height and build would be correlated to the weight by a relationship, which looks like the equation above. In this equation: Y ¿ Dependent Variable a ¿ Slope X ¿ Independent variable b ¿ Intercept These coefficients a and b are derived based on minimizing the sum of squared difference of distance between data points and regression line. Look at the below example. Here we have identified the best fit line having linear equation y=0.2811x+13.9. Now using this equation, we can find the weight, knowing the height of a person. Linear_Regression Linear Regression is of mainly two types: Simple Linear Regression and Multiple Linear Regression. Simple Linear Regression is characterized by one independent variable. And, Multiple Linear Regression(as the name suggests) is characterized by multiple (more than 1) independent variables. While finding best fit line, you can fit a polynomial or curvilinear regression. And these are known as polynomial or curvilinear regression. Python Code #Import Library #Import other necessary libraries like pandas, numpy... from sklearn import linear_model #Load Train and Test datasets #Identify feature and response variable(s) and values must be numeric and numpy arrays x_train=input_variables_values_training_datasets y_train=target_variables_values_training_datasets x_test=input_variables_values_test_datasets # Create linear regression object linear = linear_model.LinearRegression() # Train the model using the training sets and check score linear.fit(x_train, y_train) linear.score(x_train, y_train) #Equation coefficient and Intercept print('Coefficient: \n', linear.coef_) print('Intercept: \n', linear.intercept_) #Predict Output predicted= linear.predict(x_test) R Code #Load Train and Test datasets #Identify feature and response variable(s) and values must be numeric and numpy arrays x_train <- input_variables_values_training_datasets y_train <- target_variables_values_training_datasets x_test <- input_variables_values_test_datasets x <- cbind(x_train,y_train) # Train the model using the training sets and check score linear <- lm(y_train ~ ., data = x) summary(linear) #Predict Output predicted= predict(linear,x_test) 2. Logistic Regression Don¿t get confused by its name! It is a classification not a regression algorithm. It is used to estimate discrete values ( Binary values like 0/1, yes/no, true/false ) based on given set of independent variable(s). In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function. Hence, it is also known as logit regression. Since, it predicts the probability, its output values lies between 0 and 1 (as expected). Again, let us try and understand this through a simple example. Let¿s say your friend gives you a puzzle to solve. There are only 2 outcome scenarios ¿ either you solve it or you don¿t. Now imagine, that you are being given wide range of puzzles / quizzes in an attempt to understand which subjects you are good at. The outcome to this study would be something like this ¿ if you are given a trignometry based tenth grade problem, you are 70% likely to solve it. On the other hand, if it is grade fifth history question, the probability of getting an answer is only 30%. This is what Logistic Regression provides you. Coming to the math, the log odds of the outcome is modeled as a linear combination of the predictor variables. odds= p/ (1-p) = probability of event occurrence / probability of not event occurrence ln(odds) = ln(p/(1-p)) logit(p) = ln(p/(1-p)) = b0+b1X1+b2X2+b3X3....+bkXk Above, p is the probability of presence of the characteristic of interest. It chooses parameters that maximize the likelihood of observing the sample values rather than that minimize the sum of squared errors (like in ordinary regression). Now, you may ask, why take a log? For the sake of simplicity, let¿s just say that this is one of the best mathematical way to replicate a step function. I can go in more details, but that will beat the purpose of this article. Python Code #Import Library from sklearn.linear_model import LogisticRegression #Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset # Create logistic regression object model = LogisticRegression() # Train the model using the training sets and check score model.fit(X, y) model.score(X, y) #Equation coefficient and Intercept print('Coefficient: \n', model.coef_) print('Intercept: \n', model.intercept_) #Predict Output predicted= model.predict(x_test) R Code x <- cbind(x_train,y_train) # Train the model using the training sets and check score logistic <- glm(y_train ~ ., data = x,family='binomial') summary(logistic) #Predict Output predicted= predict(logistic,x_test) Furthermore.. There are many different steps that could be tried in order to improve the model: including interaction terms removing features regularization techniques using a non-linear model 3. Decision Tree This is one of my favorite algorithm and I use it quite frequently. It is a type of supervised learning algorithm that is mostly used for classification problems. Surprisingly, it works for both categorical and continuous dependent variables. In this algorithm, we split the population into two or more homogeneous sets. This is done based on most significant attributes/ independent variables to make as distinct groups as possible. For more details, you can read: Decision Tree Simplified. In the image above, you can see that population is classified into four different groups based on multiple attributes to identify ¿if they will play or not¿. To split the population into different heterogeneous groups, it uses various techniques like Gini, Information Gain, Chi-square, entropy. The best way to understand how decision tree works, is to play Jezzball ¿ a classic game from Microsoft (image below). Essentially, you have a room with moving walls and you need to create walls such that maximum area gets cleared off with out the balls. So, every time you split the room with a wall, you are trying to create 2 different populations with in the same room. Decision trees work in very similar fashion by dividing a population in as different groups as possible. Decision Tree ¿ Simplified!

Installing ROOT in Ubuntu

First Install some pre-requisite & then install ROOT

I made a simple bash script as follows

In your Home directory: Open any editor and write the following in shell file name "shell.sh"


# Harinder Singh Bawa 09/23/2015

echo "Getting required libraries...";

sudo apt-get update

sudo apt-get install git dpkg-dev make g++ gcc binutils libx11-dev libxpm-dev libxft-dev libxext-dev

sudo apt-get install gfortran libssl-dev libpcre3-dev \ xlibmesa-glu-dev libglew1.5-dev libftgl-dev \ libmysqlclient-dev libfftw3-dev cfitsio-dev \ graphviz-dev libavahi-compat-libdnssd-dev \ libldap2-dev python-dev libxml2-dev libkrb5-dev \ libgsl0-dev libqt4-dev

# remove any obsolete libraries

sudo apt-get autoremove

# Build using maximum number of physical cores

numCores=`cat /proc/cpuinfo | grep "cpu cores" | uniq | awk '{print $NF}'`

# Define install path


echo "Downloading development head version from svn..."

wget https://root.cern.ch/download/root_v5.34.32.source.tar.gz

tar xvzf root_v5.34.32.source.tar.gz

cd root

make -j $numCores

make install


echo "...done."

****Simply run "source shell.sh" in your home directory****

When you see "make install" is finished. OPen .bashrc and write "source $HOME/root/bin/thisroot.sh".

exit and open new shell and type "root"

Basic Lectures

Introduction to Programming

Physics Papers

Software installation

Statistics and Data Analysis


1 - Books

Statistical methods in experimental physics, James, Frederick, World Scientific, 2006. http://www.worldscibooks.com/physics/6096.html.
A First Course on Time Series Analysis, S. Aulbach et al., SAS, 2006. http://statistik.mathematik.uni-wuerzburg.de/timeseries/.
Bayesian Reasoning in Data Analysis, A Critical Introduction, G. D'Agostini, World Scientific, 2003. http://www.worldscibooks.com/mathematics/5262.html.
Probability Theory: The Logic of Science, E. T. Jaynes, Cambridge University Press, 2003. Edited by G. Larry Bretthorst (See also the Fragmentary Edition of June 1994, available also here, and the Unofficial Errata and Commentary). http://www.cambridge.org/asia/catalogue/catalogue.asp?isbn=9780521592710.
Kendall's Advanced Theory of Statistics, Volume 2A: Classical Inference and and the Linear Model, M. Kendall, A. Stuart, J.K. Ord, S. Arnold, Oxford University Press, 1999. Sixth Edition.
Statistical Data Analysis, Glen Cowan, Oxford University Press, 1998. http://ukcatalogue.oup.com/product/9780198501558.do.
Kendall's Advanced Theory of Statistics, Volume 1: Distribution Theory, M. Kendall, A. Stuart, J.K. Ord, Oxford University Press, 1994. Sixth Edition.
Kendall's Advanced Theory of Statistics, Volume 2B: Bayesian Inference, A. O'Hagan, Oxford University Press, 1994. First Edition.
Probability and Statistics in Experimental Physics, Byron P. Roe, Springer, 1992. http://www.springer.com/physics/complexity/book/978-0-387-95163-8.
Statistics: A Guide to the Use of Statistical Methods in the Physical Sciences, R. J. Barlow, Wiley, 1989. http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471922951.html.
Statistics for nuclear and particle physicists, Louis Lyons, Cambridge University Press, 1986. http://books.google.it/books?id=C1Vsbk5UkBAC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false.
Statistical Methods in Experimental Physics, W. T. Eadie, D. Drijard, F. E. James, M. Roos, B. Sadoulet, North Holland, 1971.
Theory of Probability, H. Jeffreys, Oxford University Press, 1961. First published in 1939.

2 - Reviews

Bayesian inference in physics, von Toussaint, Udo, Rev. Mod. Phys. 83 (2011) 943-999, American Physical Society. http://link.aps.org/doi/10.1103/RevModPhys.83.943.
Introduction to Randomness and Statistics, Alexander K. Hartmann, arXiv:0910.4545, 2009.

3 - Reviews - Conference Proceedings

Topics in statistical data analysis for high-energy physics, G. Cowan, arXiv:1012.3589, 2010. 2009 European School of High-Energy Physics, Bautzen, Germany, 14-27 Jun 2009.
Statistical methods in cosmology, Verde, Licia, Lect. Notes Phys. 800 (2010) 147-177, arXiv:0911.3105. 2nd Trans-Regio Winter school in Passo del Tonale.
Statistical techniques in cosmology, Heavens, Alan, arXiv:0906.0664, 2009. Francesco Lucchin summer school, Bertinoro, Italy, May 2009.
Basics of Feature Selection and Statistical Learning for High Energy Physics, Anselm Vossen, arXiv:0803.2344, 2008. Track 'Computational Intelligence for HEP Data Analysis' at iCSC 2006.
Conference Summary: Astronomy Perspective of Astro-Statistics, Ofer Lahav, arXiv:astro-ph/0610713, 2006. Statistical Challenges in Modern Astronomy IV, 2007.
Blind Analysis in Particle Physics, Aaron Roodman, ECONF C030908 (2003) TUIT001, arXiv:physics/0312102. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/TUIT001.pdf.
Introduction to Statistical Issues in Particle Physics, Roger Barlow, ECONF C030908 (2003) MOAT002, arXiv:physics/0311105. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/MOAT002.pdf.
Statistical problems in particle physics, astrophysics and cosmology., L. Lyons (ed.), R. P. Mount (ed.), R. Reitmeyer (ed.), 2003. PHYSTAT2003: Statistical Problems in Particle Physics, Astrophysics, and Cosmology, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/proceedings.html.
A Glossary of Selected Statistiscal Terms, H.B. Prosper, J.T. Linneman, W.A. Rolke, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/glossary.pdf.
Conference Summary Talk, R. Cousins, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/cousins.pdf.
Overview of Principles of Statistics, F. James, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/papers/james_1/index.html, http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/james1.pdf.
Advanced statistical techniques in particle physics., M. R. Whalley (ed.), L. Lyons (ed.), 2002. Conference on Advanced Statistical Techniques in Particle Physics, Durham, England, 18-22 Mar 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings.shtml.
Introduction to Monte Carlo methods, Weinzierl, Stefan, arXiv:hep-ph/0006269, 2000.
Workshop on Confidence Limits, F. James (ed.), L. Lyons (ed.), Y. Perrin (ed.), 2000. CERN "Yellow" Report CERN 2000-005. Workshop on Confidence Limits at CERN, 17-18 January 2000. http://preprints.cern.ch/cernrep/2000/2000-005/2000-005.html.
Bayesian Restoration of Digital Images Employing Markov Chain Monte Carlo a Review, K. P. N. Murthy, arXiv:cs.CV/0504037, 20cs. National Seminar on Recent Trends in Digital Image Processing and Applications, Yadava College, Madurai 28-29 Oct. 2004.

4 - Reviews - Bayesian Theory

Bayes in the sky: Bayesian inference and model selection in cosmology, Roberto Trotta, Contemp. Phys. 49 (2008) 71-104, arXiv:0803.4089.
Bayesian inference in processing experimental data: Principles and basic applications, D'Agostini, G., Rept. Prog. Phys. 66 (2003) 1383-1420, arXiv:physics/0304102.
Bayesian reasoning in high-energy physics: Principles and applications, D'Agostini, G., 1999. CERN "Yellow" report CERN 99-03. http://preprints.cern.ch/cernrep/1999/99-03/99-03.html.
Formal Rules for Selecting Prior Distributions: A Review and Annotated Bibliography, R. E. Kass, L. Wasserman, Journal of The American Statistical Association 91 (1996) 1343. http://www.stat.cmu.edu/www/cmu-stats/tr/tr583/tr583.html.
Probability and Measurement Uncertainty in Physics - a Bayesian Primer, D'Agostini, G., arXiv:hep-ph/9512295, 1995.
The Promise of Bayesian Inference for Astrophysics, T. J. Loredo, 1992. in Statistical Challenges in Modern Astronomy, ed. E.D. Feigelson and G.J. Babu, Springer-Verlag, New York, 1992, pp. 275-297. http://astrosun.tn.cornell.edu/staff/loredo/bayes/tjl.html.
From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics, T. J. Loredo, 1990. in Maximum-Entropy and Bayesian Methods, Dartmouth, 1989, ed. P. Fougere, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1990, pp. 81-142. http://astrosun.tn.cornell.edu/staff/loredo/bayes/tjl.html.

5 - Reviews - Bayesian Theory - Conference Proceedings

Lectures on Probability, Entropy, and Statistical Physics, Ariel Caticha, arXiv:0808.0012, 2008. MaxEnt 2008, the 28th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (July 8-13, 2008, Boraceia Beach, Sao Paulo, Brazil).
Bayesian analysis, Prosper, Harrison B., arXiv:hep-ph/0006356, 2000. Yellow Report CERN 2000-005, p.29-47. Workshop on Confidence Limits, CERN, 17-18 January 2000.

6 - Reviews - Frequentist Statistics

chi^2 and Linear Fits, A. Gould, arXiv:astro-ph/0310577, 2003.

7 - Fundamental Papers - Bayesian Theory

An Essay towards solving a Problem in the Doctrine of Chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, M. A. and F. R. S., Thomas Bayes, Philosophical Transactions of the Royal Society 53 (1763) 370-418. http://www.stat.ucla.edu/history/essay.pdf.

8 - Articles

Calculating error bars for neutrino mixing parameters, H. R. Burroughs, B. K. Cogswell, J. Escamilla-Roa, D. C. Latimer, D. J. Ernst, Phys. Rev. C85 (2012) 068501, arXiv:1204.1354.
The profile likelihood ratio and the look elsewhere effect in high energy physics, Gioacchino Ranucci, Nucl. Instrum. Meth. A661 (2012) 77-85, arXiv:1201.4604.
Testing the approximations described in 'Asymptotic formulae for likelihood-based tests of new physics', Eric Burns, Wade Fisher, arXiv:1110.5002, 2011.
Cancelling out systematic uncertainties, Jorge Norena, Licia Verde, Raul Jimenez, Carlos Pena-Garay, Cesar Gomez, Mon. Not. Roy. Astron. Soc. 419 (2012) 1040, arXiv:1107.0729.
Power-Constrained Limits, Glen Cowan, Kyle Cranmer, Eilam Gross, Ofer Vitells, arXiv:1105.3166, 2011.
Some ways of combining optimum interval upper limits, S. Yellin, arXiv:1105.2928, 2011.
Asymptotic formulae for likelihood-based tests of new physics, Glen Cowan, Kyle Cranmer, Eilam Gross, Ofer Vitells, Eur. Phys. J. C71 (2011) 1554, arXiv:1007.1727.
How good are your fits? Unbinned multivariate goodness-of-fit tests in high energy physics, Mike Williams, JINST 5 (2010) P09004, arXiv:1006.3019.
Formalism for Simulation-based Optimization of Measurement Errors in High Energy Physics, Yuehong Xie, arXiv:0901.3305, 2009.
An Ad-Hoc Method for Obtaining \chi^2 Values from Unbinned Maximum Likelihood Fits, M. Williams, C. A. Meyer, arXiv:0807.0015, 2008.
Averaging Results with Theoretical Uncertainties, F. C. Porter, arXiv:0806.0530, 2008.
Use of the median in Physics and Astronomy, Jean-Michel Levy, arXiv:0804.0606, 2008.
Testing Consistency of Two Histograms, Frank C. Porter, arXiv:0804.0380, 2008.
On sensitivity calculations for neutrino oscillation experiments, Jan Conrad, Nucl. Instrum. Meth. A580 (2007) 1460-1465, arXiv:0710.2969.
Estimation of experimental data redundancy and related statistics, I. Grabec, arXiv:0704.0162, 2007.
Extraction of physical laws from joint experimental data, I. Grabec, arXiv:0704.0151, 2007.
Notes on statistical separation of classes of events, Giovanni Punzi, arXiv:physics/0611219, 2006.
Why your model parameter confidences might be too optimistic - unbiased estimation of the inverse covariance matrix, J. Hartlap, P. Simon, P. Schneider, arXiv:astro-ph/0608064, 2006.
A Test for the Presence of a Signal, Wolfgang A. Rolke, Angel M. Lopez, arXiv:physics/0606006, 2006.
Optimal Data-Based Binning for Histograms, Kevin H. Knuth, arXiv:physics/0605197, 2006.
A General Theory of Goodness of Fit in Likelihood Fits, Raja, Rajendran, arXiv:physics/0509008, 2005.
On the Statistical Significance, Yongsheng Zhu, arXiv:physics/0507145, 2005.
Sifting data in the real world, Block, Martin M., Nucl. Instrum. Meth. A556 (2006) 308, arXiv:physics/0506010.
Simultaneous Least Squares Treatment of Statistical and Systematic Uncertainties, Werner M. Sun, Nucl. Instrum. Meth. A556 (2006) 325, arXiv:physics/0503050.
Late-Night Thoughts About the Significance of a Small Count of Nuclear or Particle Events, Ivan V. Anicin, arXiv:physics/0501108, 2005.
Inferring the success parameter p of a binomial model from small samples affected by background, G. D'Agostini, arXiv:physics/0412069, 2004.
Asymmetric Statistical Errors, Roger Barlow, arXiv:physics/0406120, 2004.
Computation of Confidence Levels for Exclusion or Discovery of a Signal with the Method of Fractional Event Counting, P.Bock, JHEP 01 (2007) 080, arXiv:hep-ex/0405072.
Asymmetric Uncertainties: Sources, Treatment and Potential Dangers, G. D'Agostini, arXiv:physics/0403086, 2004.
Facts, Values and Quanta, D. M. Appleby, arXiv:quant-ph/0402015, 2004.
Comments on Likelihood fits with variable resolution, Punzi, Giovanni, eConf C030908 (2003) WELT002, arXiv:physics/0401045.
Peak finding through Scan Statistics, F. Terranova, Nucl. Instrum. Meth. A519 (2004) 659, arXiv:physics/0311020.
A note on the use of the word "likelihood" in statistics and meteorology, Stephen Jewson, Anders Brix, Christine Ziehmann, arXiv:physics/0310020, 2003.
Statistical Challenges with Massive Data Sets in Particle Physics, Bruce Knuteson, Paul Padley, arXiv:hep-ex/0305064, 2003.
Unbiased cut selection for optimal upper limits in neutrino detectors: the model rejection potential technique, Hill, Gary C., Rawlins, Katherine, Astropart. Phys. 19 (2003) 393, arXiv:astro-ph/0209350.
Clustering statistics in cosmology, Martinez, Vicent J., Saar, Enn, arXiv:astro-ph/0209208, 2002.
Interpolation and smoothing, Lombardi, Marco, Astron. Astrophys. 395 (2002) 733, arXiv:astro-ph/0208533.
Finding an upper limit in the presence of unknown background, Yellin, S., Phys. Rev. D66 (2002) 032005, arXiv:physics/0203002.
Analytic marginalization over CMB calibration and beam uncertainty, Bridle, S. L. et al., Mon. Not. Roy. Astron. Soc. 335 (2002) 1193, arXiv:astro-ph/0112114.
Error estimates on parton density distributions, Botje, M., J. Phys. G28 (2002) 779-790, arXiv:hep-ph/0110123.
Frequentist and Bayesian confidence limits, Zech, Gunter, Eur. Phys. J. direct C4 (2002) 12, arXiv:hep-ex/0106023.
Uncertainties of predictions from parton distribution functions. I: The Lagrange multiplier method, Stump, D. et al., Phys. Rev. D65 (2001) 014012, arXiv:hep-ph/0101051.
Uncertainties of predictions from parton distribution functions. II: The Hessian method, Pumplin, J. et al., Phys. Rev. D65 (2001) 014013, arXiv:hep-ph/0101032.
Confronting classical and Bayesian confidence limits to examples, Zech, Gunter, arXiv:hep-ex/0004011, 2000.
Citations and the Zipf-Mandelbrot's law, Z. K. Silagadze, Complex Syst. 11 (1997) 487-499, arXiv:physics/9901035.
Expected coverage of Bayesian and classical intervals for a small number of events, Helene, O., Phys. Rev. D60 (1999) 037901.
Estimation of Asymmetry in Physics, S. Wilson, K. J. Coakley, Phys. Rev. E53 (1996) 2160-2168.
Why isn't every physicist a Bayesian?, Cousins, R. D., Am. J. Phys. 63 (1995) 398.

9 - Applications

Statistical Evaluation of Experimental Determinations of Neutrino Mass Hierarchy, X. Qian et al., arXiv:1210.3651, 2012.
Limit setting procedures and theoretical uncertainties in Higgs boson searches, Bernhard Mistlberger, Falko Dulat, arXiv:1204.3851, 2012.
Reinterpretion of Experimental Results with Basis Templates, Kanishka Rao, Daniel Whiteson, arXiv:1203.6642, 2012.
Estimating the significance of a signal in a multi-dimensional search, Ofer Vitells, Eilam Gross, Astropart. Phys. 35 (2011) 230-234, arXiv:1105.4355.
CHIWEI: A code of goodness of fit tests for weighted and unweighed histograms, Nikolai Gagunashvili, (2011), arXiv:1104.3733.
Statistical Significance of the Gallium Anomaly, Carlo Giunti, Marco Laveder, Phys. Rev. C83 (2011) 065504, arXiv:1006.3244.
On the Peirce's 'balancing reasons rule' failure in his 'large bag of beans' example, G. D'Agostini, arXiv:1003.3659, 2010.
On the so-called Boy or Girl Paradox, G. D'Agostini, arXiv:1001.0708, 2010.
On the so-called Boy or Girl Paradox, G. D'Agostini, arXiv:1001.0708, 2010.
A Bayesian Assessment of P-Values for Significance Estimation of Power Spectra and an Alternative Procedure, with Application to Solar Neutrino Data, P.A. Sturrock, J.D. Scargle, Astrophys. J. 706 (2009) 393-398, arXiv:0904.1713.
A pitfall in the use of extended likelihood for fitting fractions of pure samples in mixed samples, A. Nappi, Comput. Phys. Commun. 180 (2009) 269, arXiv:0803.2711.
A practical guide to Basic Statistical Techniques for Data Analysis in Cosmology, Verde, Licia, arXiv:0712.3028, 2007.
Forecasting neutrino masses from combining KATRIN and the CMB: Frequentist and Bayesian analyses, Ole Host, Ofer Lahav, Filipe B. Abdalla, Klaus Eitel, Phys. Rev. D76 (2007) 113005, arXiv:0709.1317.
On influence of experimental resolution on the statistical significance of a signal: implication for pentaquark searches, S.V. Chekanov, B.B. Levchenko, Phys. Rev. D76 (2007) 074025, arXiv:0707.2203.
Statistical methods applied to composition studies of ultrahigh energy cosmic rays, F. Catalani et al., Astropart. Phys. 28 (2007) 357-365, arXiv:astro-ph/0703582.
Information criteria for astrophysical model selection, Andrew R Liddle, Mon. Not. Roy. Astron. Soc. Lett. 377 (2007) L74-L78, arXiv:astro-ph/0701113.
A Likelihood Method for Measuring the Ultrahigh Energy Cosmic Ray Composition, HiRes Collaboration (HiRes), Astropart. Phys. 26 (2006) 28-40, arXiv:astro-ph/0604558.
A procedure to produce excess, probability and significance maps and to compute point-sources flux upper limits, Billoir, P., Letessier-Selvon, A., arXiv:astro-ph/0507538, 2005.
Which cosmological model with dark energy - phantom or LambdaCDM, Wlodzimierz Godlowski Marek Szydlowski, Phys. Lett. B623 (2005) 10, arXiv:astro-ph/0507322.
Higher Criticism Statistic: Detecting and Identifying Non-Gaussianity in the WMAP First Year Data, L. Cayon, J. Jin, A. Treaster, Mon. Not. Roy. Astron. Soc. 362 (2005) 826, arXiv:astro-ph/0507246.
Blind search for the real sample: Application to the origin of ultra-high energy cosmic rays, Stern, Boris E., Poutanen, Juri, Astrophys. J. 623 (2005) L33, arXiv:astro-ph/0501677.
Time series analysis in Astronomy: limits and potentialities, Vio, R., Kristensen, N. R., Madsen, H., Wamsteker, W., arXiv:astro-ph/0410367, 2004.
Nonparametric Inference for the Cosmic Microwave Background, Christopher R. Genovese et al., arXiv:astro-ph/0410140, 2004.
Probabilistic forecasts of temperature: measuring the utility of the ensemble spread, Stephen Jewson, arXiv:physics/0410039, 2004.
Three-Point Statistics from a New Perspective, Istvan Szapudi, Astrophys. J. 605 (2004) L89, arXiv:astro-ph/0404476.
sPlot: a statistical tool to unfold data distributions, Muriel Pivk, Francois R. Le Diberder, Nucl. Instrum. Meth. A555 (2005) 356, arXiv:physics/0402083.
Neutrino spin and chiral dynamics in gravitational fields, Singh, Dinesh, Phys. Rev. D71 (2005) 105003, arXiv:gr-qc/0401044.
Do probabilistic medium-range temperature forecasts need to allow for non-normality?, Stephen Jewson, arXiv:physics/0310060, 2003.
Comparing the ensemble mean and the ensemble standard deviation as inputs for probabilistic medium-range temperature forecasts, Stephen Jewson, arXiv:physics/0310059, 2003.
The statistical distribution of money and the rate of money transference, Juan C. Ferrero, arXiv:cond-mat/0306322, 2003.
Tests of Statistical Significance and Background Estimation in Gamma Ray Air Shower Experiments, R. Fleysher et al., Astrophys. J. 603 (2004) 355, arXiv:astro-ph/0306015.
Maximum likelihood analysis of the first KamLAND results, A. Ianni, J. Phys. G29 (2003) 2107, arXiv:hep-ph/0302230.
Improved approximations of Poissonian errors for high confidence levels, Harald Ebeling, Mon. Not. Roy. Astron. Soc. 349 (2004) 768, arXiv:astro-ph/0301285.
Higgs Statistics for Pedestrians, Eilam Gross, Amit Klier, arXiv:hep-ex/0211058, 2002.

10 - Applications - Conference Proceedings

Probably a discovery: Bad mathematics means rough scientific communication, G. D'Agostini, arXiv:1112.3620, 2011. University of Perugia, 15-16 April 2011 and at MAPSES School in Lecce, 23-25 November 2011.
Discovery and Upper Limits in Search for Exotic Physics with Neutrino Telescopes, Jan Conrad, arXiv:astro-ph/0612082, 2006. Workshop on Exotic Physics with Neutrino Telescope, Uppsala, Sweden, Sept. 2006.
Maximum likelihood estimation for a group of physical transformations, G. Chiribella, G. M. D'Ariano, P. Perinotti, M. F. Sacchi, arXiv:quant-ph/0507007, 2005. Workshop on "Quantum entanglement in physical and information sciences", Pisa, December 14-18, 2004.
Statistical Challenges of Cosmic Microwave Background Analysis, Benjamin D. Wandelt, ECONF C030908 (2004) THAT004, arXiv:astro-ph/0401622. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/THAT004.pdf.
Setting confidence intervals in coincidence search analysis, Lucio Baggio, Giovanni A. Prodi, ECONF C030908 (2003) WELT003, arXiv:astro-ph/0312353. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/WELT003.pdf.
Blind Analysis in Particle Physics, Roodman, Aaron, ECONF C030908 (2003) TUIT001, arXiv:physics/0312102. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/TUIT001.pdf.
Measures of Significance in HEP and Astrophysics, J. T. Linnemann, ECONF C030908 (2003) MOBT001, arXiv:physics/0312059. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/MOBT001.pdf.
Statistical Issues in Particle Physics - A View from BaBar, Frank C. Porter et al. (BaBar), ECONF C030908 (2003) WEAT002, arXiv:physics/0311092. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/WEAT002.pdf.

11 - Quantum Probability

Quantum Mechanics as Quantum Information (and only a little more), Christopher A. Fuchs, arXiv:quant-ph/0205039, 2002.
Conditions for compatibility of quantum-state assignments, Carlton M. Caves, Christopher A. Fuchs, Rudiger Schack, Phys. Rev. A66 (2002) 062111.
Unknown Quantum States: the Quantum de Finetti Representation, Carlton M. Caves, Christopher A. Fuchs, Rudiger Schack, J. Math. Phys. 43 (2002) 4537.
Quantum Probabilities as Bayesian Probabilities, Carlton M. Caves, Christopher A. Fuchs, Rudiger Schack, Phys. Rev. A65 (2002) 022305.
Notes on a Paulian Idea: Foundational, Historical, Anecdotal and Forward-Looking Thoughts on the Quantum, Christopher A. Fuchs, arXiv:quant-ph/0105039, 2001.
Quantum Probability from Decision Theory?, Barnum, H., Caves, C. M., Finkelstein, J., Fuchs, C. A., Schack, R., Proc. Roy. Soc. Lond. A456 (2000) 1175-1182, arXiv:quant-ph/9907024.
Quantum Theory of Probability and Decisions, Deutsch, David, arXiv:quant-ph/9906015, 1999.
The Statistical Interpretation of Quantum Mechanics, L. E. Ballentine, Rev. Mod. Phys. 42 (1970) 358.

12 - Bayesian Theory

Computing the Bayesian Evidence from a Markov Chain Monte Carlo Simulation of the Posterior Distribution, Martin D. Weinberg, arXiv:0911.1777, 2009.
A Quantitative Occam's Razor, R. D. Sorkin, Int. J. Theor. Phys. 22 (1983) 1091, arXiv:astro-ph/0511780.
A Hidden Markov model for Bayesian data fusion of multivariate signals, O. Feron, A. Mohammad-Djafari, arXiv:physics/0403149, 2004. presented at Fifth Int. Triennial Calcutta Symposium on Probability and Statistics, 28-31 December. 2003, Dept. of Statistics, Calcutta University, Kolkata, India.
A Bayesian approach to change point analysis of discrete time series, A. Mohammad-Djafari, O. Feron, arXiv:physics/0403148, 2004.
Probabilities as Measures of Information, F. G. Perey, arXiv:quant-ph/0310073, 2003.
A Good Measure for Bayesian Inference, H. L. Harney, arXiv:physics/0103030, 2001.
Inferring the intensity of Poisson processes at the limit of the detector sensitivity (with a case study on gravitational wave burst search), Astone, P., D'Agostini, G. D., arXiv:hep-ex/9909047, 1999.
Bayesian analysis of multi-source data, Bhat, P. C., Prosper, H. B., Snyder, S. S., Phys. Lett. B407 (1997) 73-78.
A Multidimensional unfolding method based on Bayes' theorem, D'Agostini, G. D., Nucl. Instrum. Meth. A362 (1995) 487-498.
Reply to "comment on "small signal analysis in high-energy physics: a bayesian approach".", Prosper, H. B., Phys. Rev. D38 (1988) 3584-3585.
Small signal analysis in high-energy physics: a bayesian approach, Prosper, H. B., Phys. Rev. D37 (1988) 1153-1160.
Experimental signs pointing to a bayesian instead of a classical approach for experiments with a small number of events, Escoubes, B., De Unamuno, S., Helene, O., Nucl. Instrum. Meth. A257 (1987) 346-360.
A bayesian analysis of experiments with small numbers of events, Prosper, H. B., Nucl. Instrum. Meth. A241 (1985) 236-240.

13 - Bayesian Theory - Conference Proceedings

Wrong Priors, Carlos C. Rodriguez, arXiv:0709.1067, 2007. MaxEnt2007.
Bayesian reference analysis, Demortier, L., 2005. PHYSTATO5: Statistical Problems in Particle Physics, Astrophysics and Cosmology, Oxford, England, United Kingdom, 12-15 Sep 2005. http://www.physics.ox.ac.uk/phystat05/proceedings/files/demortier-refana.ps.
From Observations to Hypotheses: Probabilistic Reasoning Versus Falsificationism and its Statistical Variations, G. D'Agostini, arXiv:physics/0412148, 2004. 2004 Vulcano Workshop on Frontier Objects in Astrophysics and Particle Physics, Vulcano (Italy) May 24-29, 2004.
Entropic Priors, A. Caticha, R. Preuss, arXiv:physics/0312131, 2003. MaxEnt'03, the 23d International Workshop on Bayesian Inference and Maximum Entropy Methods (August 3-8, 2003, Jackson Hole, WY, USA).
Relative Entropy and Inductive Inference, A. Caticha, arXiv:physics/0311093, 2003. MaxEnt23, the 23rd International Workshop on Bayesian Inference and Maximum Entropy Methods (August 3-8, 2003, Jackson Hole, WY, USA).
Why be a Bayesian?, M. Goldstein, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/goldstein.pdf.
Objectively derived default "prior" depends on stopping rule; Bayesian treatment of nuisance parameters is defended, G. Kahrimanis, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/papers/kahrimanis_defpr.pdf, http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/kahrimanis.pdf.
Bayesian reasoning versus conventional statistics in high energy physics, D'Agostini, G., arXiv:physics/9811046, 1998. 18th International Workshop on Maximum Entropy and Bayesian Methods (MaxEnt 98), Garching, Germany, 27-31 Jul 1998.
Unfolding of experimental distributions by an interactive use of Bayes's formula with intermediated smoothing, D'Agostini, G., 1995. In "Pisa 1995, New computing techniques in physics research", 697-702.

14 - Bayesian Theory - Applications

Bayesian Approach for a Neutrino Point Source Analysis, Debanjan Bose, Lionel Brayeur, Martin Casier, Geraldina Golup, Nick van Eijndhoven, arXiv:1212.2008, 2012.
A Bayesian technique for improving the sensitivity of the atmospheric neutrino L/E analysis, Blake, Andrew, Chapman, John, Thomson, Mark, arXiv:1208.2899, 2012.
Quantified naturalness from Bayesian statistics, Sylvain Fichet, Phys. Rev. D86 (2012) 125029, arXiv:1204.4940.
Bayesian Implications of Current LHC and XENON100 Search Limits for the Constrained MSSM, Andrew Fowlie, Artur Kalinowski, Malgorzata Kazana, Leszek Roszkowski, Y.-L. Sming Tsai, Phys. Rev. D85 (2012) 075012, arXiv:1111.6098.
How to Use Experimental Data to Compute the Probability of Your Theory, Georgios Choudalakis, arXiv:1110.5295, 2011.
Signal + background model in counting experiments, Diego Casadei, JINST 7 (2012) P01012, arXiv:1108.4270.
Bayesian analysis of the astrobiological implications of life's early emergence on Earth, Spiegel, David S., Turner, Edwin L., Proc.Nat.Acad.Sci. 109 (2011) 395-400, arXiv:1107.3835.
Two-Photon Exchange Effect Studied with Neural Networks, Krzysztof M. Graczyk, Phys. Rev. C84 (2011) 034314, arXiv:1106.1204.
A Bayesian view of the current status of dark matter direct searches, Chiara Arina, Jan Hamann, Yvonne Y. Y. Wong, JCAP 1109 (2011) 022, arXiv:1105.5121.
A Bayesian approach to evaluate confidence intervals in counting experiments with background, F. Loparco, M. N. Mazziotta, Nucl. Instrum. Meth. A646 (2011) 167-173, arXiv:1105.3041.
Z' Bosons at Colliders: a Bayesian Viewpoint, Jens Erler, Paul Langacker, Shoaib Munir, Eduardo Rojas, JHEP 11 (2011) 076, arXiv:1103.2659.
Bayesian Inference in the Scaling Analysis of Critical Phenomena, Kenji Harada, arXiv:1102.4149, 2011.
False-alarm probability in relation to over-sampled power spectra, with application to Super-Kamiokande solar neutrino data, Peter A. Sturrock, Jeffrey D. Scargle, Astrophys. J. 718 (2010) 527-529, arXiv:1006.0546.
A Bayesian approach to QCD sum rules, Philipp Gubler, Makoto Oka, Prog. Theor. Phys. 124 (2010) 995-1018, arXiv:1005.2459.
Direct determination of the solar neutrino fluxes from solar neutrino data, M.C. Gonzalez-Garcia, Michele Maltoni, Jordi Salvado, JHEP 05 (2010) 072, arXiv:0910.4584.
A Bayesian Analysis of the Constrained NMSSM, Daniel E. Lopez-Fogliani, Leszek Roszkowski, Roberto Ruiz de Austri, Tom A. Varley, Phys. Rev. D80 (2009) 095013, arXiv:0906.4911.
Improving Application of Bayesian Neural Networks to Discriminate Neutrino Events from Backgrounds in Reactor Neutrino Experiments, Ye Xu, WeiWei Xu, YiXiong Meng, Bin Wu, JINST 4 (2009) P01004, arXiv:0901.1497.
Bayesian optimal reconstruction of the primordial power spectrum, M. Bridges, F. Feroz, M.P. Hobson, A.N. Lasenby, arXiv:0812.3541, 2008.
Applying Bayesian Neural Network to Determine Neutrino Incoming Direction in Reactor Neutrino Experiments and Supernova Explosion Location by Scintillator Detectors, Weiwei Xu, Ye Xu, Yixiong Meng, Bin Wu, JINST 4 (2009) P01002, arXiv:0812.2713.
Bayesian approach and Naturalness in MSSM analyses for the LHC, Cabrera, M. E., Casas, J. A., de Austri, R. Ruiz, JHEP 03 (2009) 075, arXiv:0812.0536.
Bayesian Constraints on \vartheta_{13} from Solar and KamLAND Neutrino Data, H.L. Ge, C. Giunti, Q.Y. Liu, Phys. Rev. D80 (2009) 053009, arXiv:0810.5443.
MultiNest: an efficient and robust Bayesian inference tool for cosmology and particle physics, Feroz, F., Hobson, M. P., Bridges, M., (2008), arXiv:0809.3437.
Applications of Bayesian Probability Theory in Astrophysics, Brewer, Brendon J., arXiv:0809.0939, 2008.
A Bayesian approach to power-spectrum significance estimation, with application to solar neutrino data, Sturrock, P. A., arXiv:0809.0276, 2008.
Bayesian Methods for Parameter Estimation in Effective Field Theories, Schindler, Matthias R., Phillips, Daniel R., Annals Phys. 324 (2009) 682-708, arXiv:0808.3643.
Bayesian analysis of sparse anisotropic universe models and application to the 5-yr WMAP data, Nicolaas E. Groeneboom, Hans Kristian Eriksen, Astrophys. J. 690 (2009) 1807-1819, arXiv:0807.2242.
Bayesian Analysis of Solar Oscillations, M. S. Marsh, J. Ireland, T. Kucera, Astrophys. J. 681 (2008) 672-679, arXiv:0804.1447.
Limitations of Bayesian Evidence Applied to Cosmology, Efstathiou, G., arXiv:0802.3185, 2008.
Applying Bayesian Neural Networks to Event Reconstruction in Reactor Neutrino Experiments, Xu, Ye, Xu, Weiwei, Meng, Yixiong, Zhu, Kaien, Xu, Wei, Nucl. Instrum. Meth. A592 (2008) 451-455, arXiv:0712.4042.
A Bayesian analysis of pentaquark signals from CLAS data, D. G. Ireland et al. (CLAS), Phys. Rev. Lett. 100 (2008) 052001, arXiv:0709.3154.
Bayesian analysis of the low-resolution polarized 3-year WMAP sky maps, H. K. Eriksen et al., Astrophys. J. 665 (2007) L1, arXiv:0705.3643.
Bayesian reconstruction of the cosmological large-scale structure: methodology, inverse algorithms and numerical optimization, F.S. Kitaura, T.A. Ensslin, arXiv:0705.0429, 2007.
Are We Typical?, Hartle, James B., Srednicki, Mark, Phys. Rev. D75 (2007) 123523, arXiv:0704.2630.
Bayesian Estimation Applied to Multiple Species: Towards cosmology with a million supernovae, Martin Kunz, Bruce A. Bassett, Renee Hlozek, Phys. Rev. D75 (2007) 103508, arXiv:astro-ph/0611004.
The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA) and its first application: COSMOS, R. Feldmann et al., Mon. Not. Roy. Astron. Soc. 372 (2006) 565-577, arXiv:astro-ph/0609044.
Bayesian Inference from Observations of Solar-Like Oscillations, Brendon J. Brewer, Timothy R. Bedding, Hans Kjeldsen, Dennis Stello, Astrophys. J. 654 (2006) 551-557, arXiv:astro-ph/0608571.
Bayesian Statistics at Work: the Troublesome Extraction of the CKM Phase alpha, J. Charles et al., arXiv:hep-ph/0607246, 2006.
Testing the stability of the solar neutrino LMA solution with a Bayesian analysis, Chen, B. L., Ge, H. L., Giunti, C., Liu, Q. Y., Mod. Phys. Lett. A21 (2006) 2269-2282, arXiv:hep-ph/0605195.
A Bayesian model selection analysis of WMAP3, David Parkinson, Pia Mukherjee, Andrew R Liddle, Phys. Rev. D73 (2006) 123523, arXiv:astro-ph/0605003.
Bayesian analysis of Friedmannless cosmologies, Oystein Elgaroy, Tuomas Multamaki, JCAP 0609 (2006) 002, arXiv:astro-ph/0603053.
Relationalism vs. Bayesianism, Thomas Marlow, arXiv:gr-qc/0603015, 2006.
Bayesian methods of astronomical source extraction, Richard S. Savage Seb Oliver, Astrophys. J. 661 (2007) 1339-1346, arXiv:astro-ph/0512597.
A Bayesian analysis of the primordial power spectrum, M. Bridges, A.N. Lasenby, M.P. Hobson, Mon. Not. Roy. Astron. Soc. 369 (2006) 1123-1130, arXiv:astro-ph/0511573.
Bayesian estimation of pulsar parameters from gravitational wave data, Réjean J. Dupuis, Graham Woan, Phys. Rev. D72 (2005) 102002, arXiv:gr-qc/0508096.
Cosmography, Decelerating Past, and Cosmological Models: Learning the Bayesian Way, Moncy V. John, Astrophys. J. 630 (2005) 667, arXiv:astro-ph/0506284.
Applications of Bayesian Model Selection to Cosmological Parameters, Trotta, Roberto, Mon. Not. Roy. Astron. Soc. 378 (2007) 72-82, arXiv:astro-ph/0504022.
Bayesian model selection and isocurvature perturbations, Maria Beltran et al., Phys. Rev. D71 (2005) 063532, arXiv:astro-ph/0501477.
A Bayesian Estimator for Linear Calibration Error Effects in Thermal Remote Sensing, J. A. Morgan, arXiv:physics/0501087, 2005.
Bayesian evidence as a tool for comparing datasets, Phil Marshall, Nutan Rajguru, Anze Slosar, Phys. Rev. D73 (2006) 067302, arXiv:astro-ph/0412535.
Bayesian Adaptive Exploration, Thomas J. Loredo, arXiv:astro-ph/0409386, 2004.
Interval estimation in the presence of nuisance parameters. 1. Bayesian approach, Joel Heinrich et al., arXiv:physics/0409129, 2004.
Bayesian Power Spectrum Analysis of the First-Year WMAP data, I.J. O'Dwyer et al., Astrophys. J. 617 (2004) L99, arXiv:astro-ph/0407027.
Observational constraints on cosmic strings: Bayesian analysis in a three dimensional parameter space, Levon Pogosian, Mark Wyman, Ira Wasserman, JCAP 09 (2004) 008, arXiv:astro-ph/0403268.
A hidden Markov Model for image fusion and their joint segmentation in medical image computing, Olivier Feron, Ali Mohammad-Djafari, arXiv:physics/0403150, 2004.
Bayesian Estimation for Land Surface Temperature Retrieval: The Nuisance of Emissivities, John A. Morgan, arXiv:physics/0402099, 2004.
A Neural Bayesian Estimator for Conditional Probability Densities, Michael Feindt, arXiv:physics/0402093, 2004.
Revealing the Nature of Dark Energy Using Bayesian Evidence, Saini, T. D., Weller, J., Bridle, S. L., Mon. Not. Roy. Astron. Soc. 348 (2004) 603, arXiv:astro-ph/0305526.
Bayesian model comparison applied to the Explorer-Nautilus 2001 coincidence data, Astone, P., D"Agostini, G., D"Antonio, S., Class. Quant. Grav. 20 (2003) S769-S784, arXiv:gr-qc/0304096.
A Bayesian Analysis of the Cepheid Distance Scale, T. G. Barnes III et al., Astrophys. J. 592 (2003) 539, arXiv:astro-ph/0303656.
Model independent information on solar neutrino oscillations, Garzelli, M. V., Giunti, C., Phys. Rev. D65 (2002) 093005, arXiv:hep-ph/0111254.
Bayesian view of solar neutrino oscillations, Garzelli, M. V., Giunti, C., JHEP 12 (2001) 017, arXiv:hep-ph/0108191.
Bayesian analysis of neutrinos observed from supernova SN 1987A, Loredo, Thomas J., Lamb, Don Q., Phys. Rev. D65 (2002) 063002, arXiv:astro-ph/0107260.
2000 CKM-triangle analysis: A critical review with updated experimental inputs and theoretical parameters, Ciuchini, Marco et al., JHEP 07 (2001) 013, arXiv:hep-ph/0012308.
Constraints on the Higgs boson mass from direct searches and precision measurements, D'Agostini, G., Degrassi, G., Eur. Phys. J. C10 (1999) 663-675, arXiv:hep-ph/9902226.
Inferring the Spatial and Energy Distribution of Gamma-Ray Burst Sources. III. Anisotropic Models, Loredo, T. J., Wasserman, I. M., Astrophys. J. 502 (1998) 108.
How is sensory information processed?, Ilya Nemenman, arXiv:q-bio.NC/0402029, 20q-.

15 - Bayesian Theory - Applications - Conference Proceedings

Model Inference with Reference Priors, Maurizio Pierini, Harrison Prosper, Sezen Sekmen, Maria Spiropulu, arXiv:1107.2877, 2011. PHYSTAT11.
Was There a Decelerating Past for the Universe?, Moncy V. John, Aip Conf. Proc. 822 (2006) 34, arXiv:astro-ph/0509509. 1st Crisis in Cosmology Conference (CCC-1), June 23-25, 2005 at Moncao, Portugal.
Accounting for Source Uncertainties in Analyses of Astronomical Survey Data, Thomas J. Loredo, Aip Conf. Proc. 735 (2005) 195, arXiv:astro-ph/0409387. Bayesian Inference And Maximum Entropy Methods In Science And Engineering: 24th International Workshop, Garching, Germany, 2004.
MAGIC: Exact Bayesian Covariance Estimation and Signal Reconstruction for Gaussian Random Fields, Benjamin D. Wandelt, ECONF C030908 (2004) WELT001, arXiv:astro-ph/0401623. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/WELT001.pdf.
Bayesian Wavelet Based Signal and Image Separation, M. M. Ichir, A. Mohammad-Djafari, arXiv:physics/0311033, 2003. Int. Conf. on Bayesian Inference and Maximum Entropy Methods (Maxent 2003) Jackson Hole (WY), USA.
Localization of GRBs by Bayesian Analysis of Data from the HETE WXM, C. Graziani, D. Q. Lamb et al. (HETE Science Team), arXiv:astro-ph/0210429, 2002. AIP proc. "Gamma-Ray Burst and Afterglow Astronomy 2001" Woods Hole, Massachusetts.

16 - Frequentist Statistics

Negatively Biased Relevant Subsets Induced by the Most-Powerful One-Sided Upper Confidence Limits for a Bounded Physical Parameter, Robert D. Cousins, arXiv:1109.2023, 2011.
Comments on the Unified approach to the construction of Classical confidence intervals, W. Wittek, H. Bartko, T. Schweizer, arXiv:0706.3622, 2007.
Is unbiasing estimators always justified ?, Jean-Michel Levy, arXiv:hep-ph/0604133, 2006.
Transcending The Least Squares, Fyodor V. Tkachov, arXiv:physics/0604127, 2006.
Neyman and Feldman-Cousins intervals for a simple problem with an unphysical region, and an analytic solution, B.D. Yabsley, arXiv:hep-ex/0604055, 2006.
Goodness-of-fit tests in many dimensions, A. van Hameren, Nucl. Instrum. Meth. A559 (2006) 167, arXiv:physics/0405008.
Confidence Intervals with Frequentist Treatment of Statistical and Systematic Uncertainties, Wolfgang A. Rolke, Angel M. Lopez, Jan Conrad, Nucl. Instrum. Meth. A551 (2005) 493, arXiv:physics/0403059.
The uniformly most powerful test of statistical significance for counting-type experiments with background, L. Fleysher et al., arXiv:physics/0306146, 2003.
Asymmetric Systematic Errors, Roger Barlow, arXiv:physics/0306138, 2003.
Testing the statistical compatibility of independent data sets, M. Maltoni, T. Schwetz, Phys. Rev. D68 (2003) 033020, arXiv:hep-ph/0304176.
Cross-Correlation and Maximum Likelihood Analysis: A New Approach to Combine Cross-Correlation Functions, Zucker, Shay, Mon. Not. Roy. Astron. Soc. 342 (2003) 1291, arXiv:astro-ph/0303426.
A new class of binning free, multivariate goodness-of-fit tests: the energy tests, Aslan, B., Zech, G., arXiv:hep-ex/0203010, 2002.
A calculator for confidence intervals, Barlow, Roger, Comput. Phys. Commun. 149 (2002) 97-102, arXiv:hep-ex/0203002.
Including systematic uncertainties in confidence interval construction for Poisson statistics, Conrad, J., Botner, O., Hallgren, A., Perez de los Heros, Carlos, Phys. Rev. D67 (2003) 012002, arXiv:hep-ex/0202013.
The power of confidence intervals, Giunti, Carlo, Laveder, Marco, Nucl. Instrum. Meth. A480 (2002) 763, arXiv:hep-ex/0011069.
What is the usefulness of Frequentist confidence intervals?, Giunti, Carlo, arXiv:hep-ex/0003001, 2000.
The physical significance of confidence intervals, Giunti, Carlo, Laveder, Marco, Mod. Phys. Lett. 12 (2001) 1155-1168, arXiv:hep-ex/0002020.
Coverage of confidence intervals based on conditional probability, Mandelkern, M., Schultz, J., JHEP 11 (2000) 036.
The statistical analysis of Gaussian and Poisson signals near physical boundaries, Mandelkern, Mark, Schultz, Jonas, J. Math. Phys. 41 (2000) 5701-5709, arXiv:hep-ex/9910041.
Treatment of the background error in the statistical analysis of Poisson processes, Giunti, C., Phys. Rev. D59 (1999) 113009, arXiv:hep-ex/9901015.
A new ordering principle for the classical statistical analysis of Poisson processes with background, Giunti, C., Phys. Rev. D59 (1999) 053001, arXiv:hep-ph/9808240.
Small signal with background: Objective confidence intervals and regions for physical parameters from the principle of maximum likelihood, S. Ciampolillo, Nuovo Cim. A111 (1998) 1415-1430.
A Unified approach to the classical statistical analysis of small signals, Feldman, Gary J., Cousins, Robert D., Phys. Rev. D57 (1998) 3873-3889, arXiv:physics/9711021.
A Method which eliminates the discreteness in Poisson confidence limits and lessens the effect of moving cuts specifically to eliminate candidate events, Cousins, Robert, Nucl. Instrum. Meth. A337 (1994) 557-565.
Incorporating systematic uncertainties into an upper limit, Cousins, Robert D., Highland, Virgil L., Nucl. Instrum. Meth. A320 (1992) 331-335.
Statistical notes on the problem of experimental observations near an unphysical region, James, F., Roos, M., Phys. Rev. D44 (1991) 299-301.
Upper limits in experiments with background or measurement errors, Zech, G., Nucl. Instrum. Meth. A277 (1989) 608.
Clarification of the use of chi square and likelihood functions in fits to histograms, Baker, Steve, Cousins, Robert D., Nucl. Instrum. Meth. 221 (1984) 437-442.
Interpretation of the shape of the likelihood function around its minimum, James, F., Comput. Phys. Commun. 20 (1980) 29-35.
Errors on ratios of small numbers of events, James, Frederick, Roos, Matts, Nucl. Phys. B172 (1980) 475.
A Monte Carlo method for the analysis of low statistics experiments, Zech, G., Nucl. Instr. Meth. 157 (1978) 551.
"MINUIT" a system for function minimization and analysis of the parameter errors and correlations, James, F., Roos, M., Comput. Phys. Commun. 10 (1975) 343-367.

17 - Frequentist Statistics - Conference Proceedings

About the proof of the so called exact classical confidence intervals. Where is the trick?, G. D'Agostini, arXiv:physics/0605140, 2006. Lectures to graduate students at the University of Rome "La Sapienza".
Ordering Algorithms and Confidence Intervals in the Presence of Nuisance Parameters, Punzi, Giovanni, arXiv:physics/0511202, 2005. PhyStat2005, Oxford, UK, Sept 2005.
Constructing Ensembles of Pseudo-Experiments, Luc Demortier, ECONF C030908 (2003) WEMT003, arXiv:physics/0312100. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/WEMT003.pdf.
An Unbinned Goodness-of-Fit Test Based on the Random Walk, K. Kinoshita, ECONF C030908 (2003) MOCT002, arXiv:physics/0312014. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/MOCT002.pdf.
Pitfalls of Goodness-of-Fit from Likelihood, Joel Heinrich, ECONF C030908 (2003) MOCT001, arXiv:physics/0310167. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/MOCT001.pdf.
Frequentist Hypothesis Testing with Background Uncertainty, K. S. Cranmer, ECONF C030908 (2003) WEMT004, arXiv:physics/0310108. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/WEMT004.pdf.
The Power of Confidence Intervals, C. Giunti, M. Laveder, arXiv:hep-ex/0206047, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/papers/giunti_clw.ps.gz, http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/giunti2.pdf.
Limits setting in difficult cases and the Strong Confidence approach, Giovanni Punzi, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/papers/punzi_Durham.ppt, http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/punzi.pdf.
Coverage of Confidence Intervals calculated in the Presence of Systematic Uncertainties, Jan Conrad et al., 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/papers/conrad_durham.ps.gz, http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/conrad.pdf.
An Application of the Strong Confidence to the CHOOZ Experiment with Frequentist Inclusion of Systematics, D. Nicolo nd G. Signorelli, 2002. Advanced Statistical Techniques in Particle Physics, Durham, UK, 18-22 March 2002. http://www.ippp.dur.ac.uk/Workshops/02/statistics/papers/signorelli_durham.ps.gz, http://www.ippp.dur.ac.uk/Workshops/02/statistics/proceedings/signorelli.pdf.

18 - Frequentist Statistics - Applications

Combining Neutrino Oscillation Experiments with the Feldman-Cousins Method, A. V. Waldron, M. D. Haigh, A. Weber, New J. Phys. 14 (2012) 063037, arXiv:1204.3450.
Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0, J. Lundberg, J. Conrad, W. Rolke, A. Lopez, Comput. Phys. Commun. 181 (2010) 683-686, arXiv:0907.3450.
What is the probability that \theta_{13} and CP violation will be discovered in future neutrino oscillation experiments?, Thomas Schwetz, Phys. Lett. B648 (2007) 54-59, arXiv:hep-ph/0612223.
Frequentistic quantum state discrimination, Giacomo Mauro D'Ariano, Massimiliano Federico Sacchi, Jonas Kahn, arXiv:quant-ph/0504048, 2005.
A Maximum Likelihood Analysis of the Low CMB Multipoles from WMAP, G. Efstathiou, Mon. Not. Roy. Astron. Soc. 348 (2004) 885, arXiv:astro-ph/0310207.
Variations on KamLAND: likelihood analysis and frequentist confidence regions, Thomas Schwetz, Phys. Lett. B577 (2003) 120, arXiv:hep-ph/0308003.
Statistics of the Chi-Square Type, with Application to the Analysis of Multiple Time-Series Power Spectra, Sturrock, P. A., Wheatland, M. S., arXiv:astro-ph/0307353, 2003.
A Modified chi^2-Test for CMB Analyses, J.A.Rubino-Martin, J.Betancort-Rijo, Mon. Not. Roy. Astron. Soc. 345 (2003) 221, arXiv:astro-ph/0307010.
Estimation of Goodness-of-Fit in Multidimensional Analysis Using Distance to Nearest Neighbor, Ilya Narsky, arXiv:physics/0306171, 2003.
Goodness-of-fit Statistics and CMB Data Sets, M. Douspis, J.G. Bartlett, A. Blanchard, Astron. Astrophys. 410 (2003) 11, arXiv:astro-ph/0305428.
Uncertainties of predictions from parton distributions. I: experimental errors, Martin, A. D., Roberts, R. G., Stirling, W. J., Thorne, R. S., Eur. Phys. J. C28 (2003) 455, arXiv:hep-ph/0211080.

19 - Frequentist Statistics - Applications - Conference Proceedings

Multivariate Analysis from a Statistical Point of View, K. S. Cranmer, ECONF C030908 (2003) WEJT002, arXiv:physics/0310110. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/WEJT002.pdf.

20 - Bootstrap - Applications

Nonparametric Methods for Doubly Truncated Data, Efron, Bradley, Petrosian, Vahe, arXiv:astro-ph/9808334, 1998.
Application of the bootstrap statistical method to the tau decay mode problem, Hayes, K. G., Perl, Martin L., Efron, Bradley, Phys. Rev. D39 (1989) 274.

21 - Conference Proceedings

Likelihood ratio intervals with Bayesian treatment of uncertainties: coverage, power and combined experiments, Jan Conrad, Fredrik Tegenfeldt, arXiv:physics/0511055, 2005. PhyStat2005, Oxford, UK, Sept 2005, Imperial College Press.
Deriving laws from ordering relations, Kevin H. Knuth, arXiv:physics/0403031, 2004. Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Jackson Hole WY 2003.
A Measure of the Goodness of Fit in Unbinned Likelihood Fits; End of Bayesianism?, Rajendran Raja, ECONF C030908 (2003) MOCT003, arXiv:physics/0401133. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/MOCT003.pdf.
Asymmetric Errors, Barlow, Roger, ECONF C030908 (2003) WEMT002, arXiv:physics/0401042. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/WEMT002.pdf.
A unified approach to understanding statistics, James, Fred, ECONF C030908 (2003) THAT002. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/THAT002.pdf.
Definition and treatment of systematic uncertainties in high energy physics and astrophysics, Sinervo, Pekka, ECONF C030908 (2003) TUAT004. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/TUAT004.pdf.
Likelihood inference in the presence of nuisance parameters, N. Reid, D.A.S. Fraser, ECONF C030908 (2003) THAT001. PHYSTAT2003, SLAC, Stanford, Ca, USA, 8-11 Sep 2003. http://www.slac.stanford.edu/econf/C030908/papers/THAT001.pdf.
Signal Significance in Particle Physics, Sinervo, Pekka K., arXiv:hep-ex/0208005, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Signal significance in the presence of systematic and statistical uncertainties, Bityukov, S. I., JHEP 09 (2002) 060, arXiv:hep-ph/0207130. 8th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2002), Moscow, Russia, 24-28 June 2002.
Systematic errors: Facts and fictions, Barlow, Roger, arXiv:hep-ex/0207026, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Statistical practice at the Belle experiment, and some questions, Yabsley, Bruce, J. Phys. G28 (2002) 2733, arXiv:hep-ex/0207006. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Uncertainties on parton related quantities, Thorne, R. S., J. Phys. G28 (2002) 2705-2716, arXiv:hep-ph/0205235. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Questions on uncertainties in parton distributions, Thorne, R. S. et al., J. Phys. G28 (2002) 2717-2722, arXiv:hep-ph/0205233. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Uncertainties and Discovery Potential in Planned Experiments, Bityukov, S. I., Krasnikov, N. V., arXiv:hep-ph/0204326, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Answers to Fred James" "Comments on a paper by Garzelli and Giunti', Giunti, C., 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Comments on a paper by Garzelli and Giunti, James, F., 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Confidence Limits and their Errors, Rajendran Raja, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.
Credibility of Confidence Intervals, Dean Karlen, 2002. Advanced Statistical Techniques in Particle Physics, Durham, March 2002. Advanced Statistical Techniques in Particle Physics'>Advanced Statistical Techniques in Particle Physics.

22 - History

The Fermi's Bayes Theorem, G. D'Agostini, arXiv:physics/0509080, 2005.

Useful Links

Review of Particle Physics: Probability and Statistic

Uncertainty of Measurement Results from NIST

BIPS: Bayesian Inference for the Physical Sciences

Giulio D'Agostini page on Probability and Statistics

International Society for Bayesian Analysis

Probability Theory As Extended Logic

Probability Theory: The Logic of Science by E. T. Jaynes, Fragmentary Edition of June 1994 (available also here) and the Unofficial Errata and Commentary

Probability Tutorials

CDF Statistics Committee

IPP Bayesian Data Analysis Group

BaBar Statistics Working Group web page

Carnegie Mellon Department of Statistics Technical Reports

Introduction to Probability, by Charles Grinstead and J. Laurie Snell

Bayes's Theorem, Stanford Encyclopedia of Philosophy

Workshop on Confidence Limits, CERN Yellow Report 2000-005, FNAL Confidence Limits Workshop, Durham Conference on `Advanced Statistical Techniques in Particle Physics', PHYSTAT 03, PHYSTAT 05

Some Useful Links