Stefano Andreon is an astronomer of the National Institute of Astrophysics, Brera Observatory (Milan, Italy). Stefano's research is focused on understanding the evolution of galaxies and of galaxy clusters, near and far, and adopting Bayesian methods. He also teaches Bayesian methods to PhD students of various Italian and French Universities, is a Member of the Executive Board of International Astrostatistics Association, and is first author of more than 50 referred papers.Brian Weaver is a scientist with the Statistical Sciences group at Los Alamos National Laboratory. His research interests include Monte Carlo methods, parallel computing, Bayesian design of experiments, dynamic linear models, model calibration, and applying statistics to the physical and engineering sciences. He is a mentor to both graduate and undergraduate students in statistics at Los Alamos and is a recipient of the Llyod S. Nelson award.

• ​Recipes.- A Bit of Theory.- A Bit of Numerical Computation.- Single Parameter Models.- The Prior.- Multi-parameters Models.- Non-random Data Collection.- Fitting Regression Models.- Model Checking and Sensitivity Analysis.- Bayesian vs Simple Methods.- Appendix: Probability Distributions.- Appendix: The third axiom of probability, conditional probability, independence and conditional independence.

"Andreon and Weaver ... have written a book that could be a valuable component in the new Computational Data Analysis course. ... Bayesian Methods for the Physical Sciences begins with basic probability calculus and introduces complex models and concepts as it goes along. ... Most of the content is presented through real-world examples that could easily be adopted or adapted to new tasks." (David W. Hogg, Physics Today, Issue 6, June, 2016)

Stefano Andreon is an astronomer of the National Institute of Astrophysics, Brera Observatory (Milan, Italy). Stefano's research is focused on understanding the evolution of galaxies and of galaxy clusters, near and far, and adopting Bayesian methods. He also teaches Bayesian methods to PhD students of various Italian and French Universities, is a Member of the Executive Board of International Astrostatistics Association, and is first author of more than 50 referred papers.Brian Weaver is a scientist with the Statistical Sciences group at Los Alamos National Laboratory. His research interests include Monte Carlo methods, parallel computing, Bayesian design of experiments, dynamic linear models, model calibration, and applying statistics to the physical and engineering sciences. He is a mentor to both graduate and undergraduate students in statistics at Los Alamos and is a recipient of the Llyod S. Nelson award.

• ​Recipes.- A Bit of Theory.- A Bit of Numerical Computation.- Single Parameter Models.- The Prior.- Multi-parameters Models.- Non-random Data Collection.- Fitting Regression Models.- Model Checking and Sensitivity Analysis.- Bayesian vs Simple Methods.- Appendix: Probability Distributions.- Appendix: The third axiom of probability, conditional probability, independence and conditional independence.

"Andreon and Weaver ... have written a book that could be a valuable component in the new Computational Data Analysis course. ... Bayesian Methods for the Physical Sciences begins with basic probability calculus and introduces complex models and concepts as it goes along. ... Most of the content is presented through real-world examples that could easily be adopted or adapted to new tasks." (David W. Hogg, Physics Today, Issue 6, June, 2016)

Stefano Andreon is an astronomer of the National Institute of Astrophysics, Brera Observatory (Milan, Italy). Stefano's research is focused on understanding the evolution of galaxies and of galaxy clusters, near and far, and adopting Bayesian methods. He also teaches Bayesian methods to PhD students of various Italian and French Universities, is a Member of the Executive Board of International Astrostatistics Association, and is first author of more than 50 referred papers.Brian Weaver is a scientist with the Statistical Sciences group at Los Alamos National Laboratory. His research interests include Monte Carlo methods, parallel computing, Bayesian design of experiments, dynamic linear models, model calibration, and applying statistics to the physical and engineering sciences. He is a mentor to both graduate and undergraduate students in statistics at Los Alamos and is a recipient of the Llyod S. Nelson award.

• ​Recipes.- A Bit of Theory.- A Bit of Numerical Computation.- Single Parameter Models.- The Prior.- Multi-parameters Models.- Non-random Data Collection.- Fitting Regression Models.- Model Checking and Sensitivity Analysis.- Bayesian vs Simple Methods.- Appendix: Probability Distributions.- Appendix: The third axiom of probability, conditional probability, independence and conditional independence.

"Andreon and Weaver ... have written a book that could be a valuable component in the new Computational Data Analysis course. ... Bayesian Methods for the Physical Sciences begins with basic probability calculus and introduces complex models and concepts as it goes along. ... Most of the content is presented through real-world examples that could easily be adopted or adapted to new tasks." (David W. Hogg, Physics Today, Issue 6, June, 2016)