A Student’s Guide to Bayesian Statistics

✍ Scribed by Ben Lambert

Year: 2018
Tongue: English
Leaves: 682
Category: Library

No coin nor oath required. For personal study only.

✦ Table of Contents

Half Title
Publisher Note
Title Page
Copyright Page
Acknowledgements
Contents
Online resources
Acknowledgements
About the Author
1 How Best to use this Book
Part I An Introduction to Bayesian Inference
2 The Subjective Worlds of Frequentist and Bayesian Statistics
3 Probability – The Nuts and Bolts of Bayesian Inference
Part II Understanding the Bayesian Formula
4 Likelihoods
5 Priors
6 The Devil is in the Denominator
7 The Posterior – The Goal of Bayesian Inference
Part III Analytic Bayesian Methods
8 An Introduction to Distributions for the Mathematically Uninclined
9 Conjugate priors
10 Evaluation of model fit and hypothesis testing
11 Making Bayesian analysis objective?
Part IV A practical guide to doing real-life Bayesian analysis: computational Bayes
12 Leaving conjugates behind: Markov chain Monte Carlo
13 Random Walk Metropolis
14 Gibbs sampling
15 Hamiltonian Monte Carlo
16 Stan
Part V Hierarchical models and regression
17 Hierarchical models
18 Linear regression models
19 Generalised linear models and other animals
References
Index

📜 SIMILAR VOLUMES

A Student’s Guide to Bayesian Statistics

📁 A Student’s Guide to Bayesian Statistics

✍ Ben Lambert 📂 Library 📅 2018 🏛 SAGE Publications Ltd 🌐 English

<p><span>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.<br> <br> Without sacrificing technical integrity for the sake of simplicity,

A student’s guide to Bayesian statistics

📁 A student’s guide to Bayesian statistics

✍ Lambert, Ben 📂 Library 📅 2018 🏛 Sage 🌐 English

Doing Statistical Analysis: A Student’s

📁 Doing Statistical Analysis: A Student’s Guide To Quantitative Research

✍ Christer Thrane 📂 Library 📅 2023 🏛 Routledge | Taylor & Francis Group 🌐 English

Doing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational, and inferential – and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples su

Doing Statistical Analysis. A Student’s

📁 Doing Statistical Analysis. A Student’s Guide to Quantitative Research

✍ Christer Thrane 📂 Library 📅 2023 🏛 Routledge 🌐 English

Bayesian Networks: A Practical Guide to

📁 Bayesian Networks: A Practical Guide to Applications (Statistics in Practice)

✍ Olivier Pourret, Patrick Naïm, Bruce Marcot 📂 Library 📅 2008 🏛 Wiley 🌐 English

Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis.This book provides a general int

Bayesian core : a practical approach to

📁 Bayesian core : a practical approach to computational Bayesian statistics

✍ Jean-Michel Marin, Christian P. Robert 📂 Library 📅 2007 🏛 Springer 🌐 English

"This Bayesian modeling book is intended for practitioners and applied statisticians looking for a self-contained entry to computational Bayesian statistics. Focusing on standard statistical models and backed up by discussed real datasets available from the book's Web site, it provides an operationa