Bayesian Methods for Statistical Analysis
by Borek Puza
Publisher: ANU Press 2015
Number of pages: 697
A book on statistical methods for analysing a wide variety of data. Topics: bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code.
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by Denis Anthony - BookBoon
This is a practical book. It is aimed at people who need to understand statistics, but not develop it as a subject. The typical reader might be a postgraduate student in health, life, or social science who has no knowledge of statistics.
by Benjamin Yakir - The Hebrew University of Jerusalem
This is an introduction to statistics, with R, without calculus. The target audience for this book is college students who are required to learn statistics, students with little background in mathematics and often no motivation to learn more.
by Mohammad Saber Fallah Nezhad (ed.) - InTech
Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. The purpose of this volume is to provide some applications of Bayesian optimization and dynamic programming.
by Pete Kaslik
Contents: Statistical Reasoning; Obtaining Useful Evidence; Examining the Evidence Using Graphs and Statistics; Inferential Theory; Testing Hypotheses; Confidence Intervals and Sample Size; Analysis of Bivariate Quantitative Data; Chi Square; etc.