Statistical rethinking : a Bayesian course with examples in R and Stan / Richard McElreath.

Format:

Book

Author/Creator:

McElreath, Richard, 1973- author.

Series:

Texts in statistical science.

Texts in statistical science series

Language:

English

Subjects (All):

Bayesian statistical decision theory.

R (Computer program language).

Physical Description:

1 online resource (612 pages).

Edition:

Second edition.

Place of Publication:

Boca Raton : Chapman & Hall/CRC, 2020.

Summary:

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

Contents:

The Golem of Prague

Small worlds and large worlds

Sampling the imaginary

Geocentric models

The many variables & the spurious waffles

The haunted DAG & the causal terror

Ulysses' compass

Conditional manatees

Markov chain Monte Carlo

Big entropy and the generalized linear model

God spiked the integers

Monsters and mixtures

Models with memory

Adventures in covariance

Missing data and other opportunities

Generalized linear madness

Horoscopes.

Notes:

Previous edition: Boca Raton: CRC Press/Taylor & Francis Group, 2016.

Includes bibliographical references and indexes.

Description based on print version record.

ISBN:

9780429642319

0429642318

9780429639142

0429639147

9780429029608

0429029608

OCLC:

1145123627