CoChat
Sign Up
Cited by 1,959Statistical Methods and Bayesian InferenceData Analysis with RMarkov Chains and Monte Carlo Methods

The DOI matches its Crossref record.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan

McElreath, Richard·2020·Chapman & Hall/CRC eBooks
DOI 10.1201/9780429029608
Open this paper in CoChat — free account

Abstract

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

Overview

Integrity signals

Registry

Crossref · 10.1201/9780429029608

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cited by

Sorted by citations

Items 601–700 of 1,001

Related

See all

Metadata

Authors
McElreath, R.
Affiliations
Max Planck Institute for Evolutionary Anthropology (DE)
Corresponding author
McElreath, Richard
ORCID
McElreath, Richard
Venue
Chapman & Hall/CRC eBooks
Publisher
Chapman and Hall/CRC
Published
2020-03-13
DOI
10.1201/9780429029608
OpenAlex
W4232976691
Cited by
1,959
References
0
Citations by year
2026: 297 · 2025: 385 · 2024: 325 · 2023: 317 · 2022: 268 · 2021: 223 · 2020: 96
FWCI
101.46
Type
book
Language
en
Open access
closed
Locations
Chapman & Hall/CRC eBooks (publishedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Data Analysis with R
  • Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
Keywords
Bayesian inference, causal inference, directed acyclic graphs, maximum entropy, information theory, measurement error, missing data, Gaussian processes, spatial confounding, splines, social relations model, cross-validation, importance sampling, instrumental variables, Hamiltonian Monte Carlo, Stan, R

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review