A student's guide to Bayesian Statistics
Lambert, Ben
A student's guide to Bayesian Statistics by Ben Lambert - London : SAGE, ©2018 - xx, 498 p. : ill. ; 25 cm.
This book includes bibliographical references and an index.
An introduction to Bayesian inference -- Understanding the Bayesian formula -- Analytic Bayesian methods -- A practical guide to doing real-life Bayesian analysis: Computational Bayes -- Hierarchical models and regression. 1: How to best use this book 2: The subjective worlds of Frequentist and Bayesian statistics 3: Probability - the nuts and bolts of Bayesian inference 4: Likelihoods 5: Priors 6: The devil’s in the denominator 7: The posterior - the goal of Bayesian inference 8: An introduction to distributions for the mathematically-un-inclined 9: Conjugate priors 10: Evaluation of model fit and hypothesis testing 11: Making Bayesian analysis objective? 12: Leaving conjugates behind: Markov Chain Monte Carlo 13: Random Walk Metropolis 14: Gibbs sampling 15: Hamiltonian Monte Carlo 16: Stan 17: Hierarchical models 18: Linear regression models 19: Generalised linear models and other animals Bibliography Index
"Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference, Understanding Bayes' rule, Nuts and bolts of Bayesian analytic methods, Computational Bayes and real-world Bayesian analysis, Regression analysis and hierarchical methods. This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses." --
9781473916364
2017942214
Bayesian statistical decision theory.
Bayesian statistical decision theory.
QA279.5 / .L36 2018
519.542 / LAM-S
A student's guide to Bayesian Statistics by Ben Lambert - London : SAGE, ©2018 - xx, 498 p. : ill. ; 25 cm.
This book includes bibliographical references and an index.
An introduction to Bayesian inference -- Understanding the Bayesian formula -- Analytic Bayesian methods -- A practical guide to doing real-life Bayesian analysis: Computational Bayes -- Hierarchical models and regression. 1: How to best use this book 2: The subjective worlds of Frequentist and Bayesian statistics 3: Probability - the nuts and bolts of Bayesian inference 4: Likelihoods 5: Priors 6: The devil’s in the denominator 7: The posterior - the goal of Bayesian inference 8: An introduction to distributions for the mathematically-un-inclined 9: Conjugate priors 10: Evaluation of model fit and hypothesis testing 11: Making Bayesian analysis objective? 12: Leaving conjugates behind: Markov Chain Monte Carlo 13: Random Walk Metropolis 14: Gibbs sampling 15: Hamiltonian Monte Carlo 16: Stan 17: Hierarchical models 18: Linear regression models 19: Generalised linear models and other animals Bibliography Index
"Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference, Understanding Bayes' rule, Nuts and bolts of Bayesian analytic methods, Computational Bayes and real-world Bayesian analysis, Regression analysis and hierarchical methods. This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses." --
9781473916364
2017942214
Bayesian statistical decision theory.
Bayesian statistical decision theory.
QA279.5 / .L36 2018
519.542 / LAM-S