John Thompson is professor of genetic epidemiology at the University of Leicester and has many years experience working as a biostatistician on epidemiological projects.

"… the first comprehensive guide to employing Bayesian methods using Stata statistical software. … until this book, there has been no unified presentation of how to implement Bayesian methods using Stata. … A nice feature of the book is the use of real data … I recommend it for Stata users who wish to employ Bayesian modeling within the Stata environment."—International Statistical Review, 2015

• List of figures. List of tables. Preface. Acknowledgments. The problem of priors. Evaluating the posterior. Metropolis–Hastings. Gibbs sampling. Assessing convergence. Validating the Stata code and summarizing the results. Using WinBUGS for model fitting. Model checking. Model selection. Further case studies. Writing Stata programs for specific Bayesian analysis. A Standard distributions. References . Author index . Subject index.

John Thompson is professor of genetic epidemiology at the University of Leicester and has many years experience working as a biostatistician on epidemiological projects.

"… the first comprehensive guide to employing Bayesian methods using Stata statistical software. … until this book, there has been no unified presentation of how to implement Bayesian methods using Stata. … A nice feature of the book is the use of real data … I recommend it for Stata users who wish to employ Bayesian modeling within the Stata environment."—International Statistical Review, 2015

• List of figures. List of tables. Preface. Acknowledgments. The problem of priors. Evaluating the posterior. Metropolis–Hastings. Gibbs sampling. Assessing convergence. Validating the Stata code and summarizing the results. Using WinBUGS for model fitting. Model checking. Model selection. Further case studies. Writing Stata programs for specific Bayesian analysis. A Standard distributions. References . Author index . Subject index.

John Thompson is professor of genetic epidemiology at the University of Leicester and has many years experience working as a biostatistician on epidemiological projects.

"… the first comprehensive guide to employing Bayesian methods using Stata statistical software. … until this book, there has been no unified presentation of how to implement Bayesian methods using Stata. … A nice feature of the book is the use of real data … I recommend it for Stata users who wish to employ Bayesian modeling within the Stata environment."—International Statistical Review, 2015

• List of figures. List of tables. Preface. Acknowledgments. The problem of priors. Evaluating the posterior. Metropolis–Hastings. Gibbs sampling. Assessing convergence. Validating the Stata code and summarizing the results. Using WinBUGS for model fitting. Model checking. Model selection. Further case studies. Writing Stata programs for specific Bayesian analysis. A Standard distributions. References . Author index . Subject index.