统计反思 用R和Stan例解贝叶斯方法 英文原版 Statistical Rethinking Richard McElreath

基本信息

By (author) Richard McElreath

Format Hardback | 594 pages

Dimensions 178 x 254 x 33.02mm | 1,420g

Publication date 17 Mar 2020

Publisher Taylor & Francis Ltd

Imprint CRC Press

Publication City/Country London, United Kingdom

Language English

Edition New edition

Edition Statement 2nd New edition

ISBN10 036713991X

ISBN13 9780367139919

页面参数仅供参考，具体以实物为准

内容简介

本书从贝叶斯的角度介绍了广义线性分层模型，通过贝叶斯概率和大熵的基础逻辑解释模型，内容涵盖从基本的回归分析到多层模型。此外，作者还讨论了测量误差、缺失数据以及处理空间和网络自相关的高斯过程模型。

本书以R和Stan为基础，以R代码为例，提供了一个实际的统计推断的基础。适合统计、数学等相关专业的高年级本科生、研究生，以及数据挖掘的从业人士阅读。

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.

The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding.

The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples. The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.

Features

Integrates working code into the main text

Illustrates concepts through worked data analysis examples

Emphasizes understanding assumptions and how assumptions are reflected in code

Offers more detailed explanations of the mathematics in optional sections

Presents examples of using the dagitty R package to analyze causal graphs

Provides the rethinking R package on the author's website and on GitHub

作者简介

理查德·麦克尔里思(Richard McElreath) 著：理查德·麦克尔里思(Richard McElreath )是马克斯·普朗克进化人类学研究所人类行为、生态和文化系主任。他还是加州大学戴维斯分校的人类学教授。他的研究兴趣着眼于进化和文化人类学的交叉领域，研究人类社会学习能力的进化是如何导致人类不寻常的适应力以及庞大且多样的人类社群的。

Richard McElreath studies human evolutionary ecology and is a Director at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. He has published extensively on the mathematical theory and statistical analysis of social behavior, including his first book (with Robert Boyd), Mathematical Models of Social Evolution.

书籍目录

Preface to the Second Edition

Preface

Audience

Teaching strategy

How to use this book

Installing the rethinking R package

Acknowledgments

Chapter 1. The Golem of Prague

Statistical golems

Statistical rethinking

Tools for golem engineering

Summary

Chapter 2. Small Worlds and Large Worlds

The garden of forking data

Building a model

Components of the model

Making the model go

Summary

Practice

Chapter 3. Sampling the Imaginary

Sampling from a grid-appromate posterior

Sampling to summarize

Sampling to simulate prediction

Summary

Practice

Chapter 4. Geocentric Models

Why normal distributions are normal

A language for describing models

Gaussian model of height

Linear prediction

Curves from lines

Summary

Practice

Chapter 5. The Many Variables & The Spurious Waffles

Spurious association

Masked relationship

Categorical variables

Summary

Practice

Chapter 6. The Haunted DAG & The Causal Terror

Multicollinearity

Post-treatment bias

Collider bias

Confronting confounding

Summary

Practice

Chapter 7. Ulysses' Compass

The problem with parameters

Entropy and accuracy

Golem Taming: Regularization

Predicting predictive accuracy

Model comparison

Summary

Practice

Chapter 8. Conditional Manatees

Building an interaction

Symmetry of interactions

Continuous interactions

Summary

Practice

Chapter 9. Markov Chain Monte Carlo

Good King Markov and His island kingdom

Metropolis Algorithms

Hamiltonian Monte Carlo

Easy HMC: ulam

Care and feeding of your Markov chain

Summary

Practice

Chapter 10. Big Entropy and the Generalized Linear Model

Mamum entropy

Generalized linear models

Mamum entropy priors

Summary

Chapter 11. God Spiked the Integers

Binomial regression

Poisson regression

Multinomial and categorical models

Summary

Practice

Chapter 12. Monsters and Mixtures

Over-dispersed counts

Zero-inflated outcomes

Ordered categorical outcomes

Ordered categorical predictors

Summary

Practice

Chapter 13. Models With Memory

Example: Multilevel tadpoles

Varying effects and the underfitting/overfitting trade-off

More than one type of cluster

Divergent transitions and non-centered priors

Multilevel posterior predictions

Summary

Practice

Chapter 14. Adventures in Covariance

Varying slopes by construction

Advanced varying slopes

Instruments and causal designs

Social relations as correlated varying effects

Continuous categories and the Gaussian process

Summary

Practice

Chapter 15. Missing Data and Other Opportunities

Measurement error

Missing data

Categorical errors and discrete absences

Summary

Practice

Chapter 16. Generalized Linear Madness

Geometric people

Hidden minds and observed behavior

Ordinary differential nut cracking

Population dynamics

Summary

Practice

Chapter 17. Horoscopes

Endnotes