# Statistical rethinking: a Bayesian course with examples in R and Stan

##### By: McElreath, Richard

Material type: TextSeries: Texts in Statistical SciencePublisher: Boca Raton CRC Press 2016Description: xvii, 469 pISBN: 9781482253443Subject(s): Bayesian statistical decision theory | R - Computer program languageDDC classification: 519.542 Summary: Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas. (https://www.crcpress.com/Statistical-Rethinking-A-Bayesian-Course-with-Examples-in-R-and-Stan/McElreath/p/book/9781482253443)Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|

Books | Vikram Sarabhai Library | Slot 1676 (2 Floor, East Wing) | Non-fiction | 519.542 M2S8 (Browse shelf) | Available | 192803 |

Table of Contents:

1.The Golem of Prague

Statistical golems

Statistical rethinking

Three tools for golem engineering

Summary

2.Small Worlds and Large Worlds

The garden of forking data

Building a model

Components of the model

Making the model go

Summary

Practice

3.Sampling the Imaginary

Sampling from a grid-approximate posterior

Sampling to summarize

Sampling to simulate prediction

Summary

Practice

4.Linear Models

Why normal distributions are normal

A language for describing models

A Gaussian model of height

Adding a predictor

Polynomial regression

Summary

Practice

5.Multivariate Linear Models

Spurious association

Masked relationship

When adding variables hurts

Categorical variables

Ordinary least squares and lm

Summary

Practice

6.Overfitting, Regularization, and Information Criteria

The problem with parameters

Information theory and model performance

Regularization

Information criteria

Using information criteria

Summary

Practice

7.Interactions

Building an interaction

Symmetry of the linear interaction

Continuous interactions

Interactions in design formulas

Summary

Practice

8.Markov Chain Monte Carlo

Good King Markov and His island kingdom

Markov chain Monte Carlo

Easy HMC: map2stan

Care and feeding of your Markov chain

Summary

Practice

9.Big Entropy and the Generalized Linear Model

Maximum entropy

Generalized linear models

Maximum entropy priors

Summary

10.Counting and Classification

Binomial regression

Poisson regression

Other count regressions

Summary

Practice

11.Monsters and Mixtures

Ordered categorical outcomes

Zero-inflated outcomes

Over-dispersed outcomes

Summary

Practice

12.Multilevel Models

Example: Multilevel tadpoles

Varying effects and the underfitting/overfitting trade-off

More than one type of cluster

Multilevel posterior predictions

Summary

Practice

13.Adventures in Covariance

Varying slopes by construction

Example: Admission decisions and gender

Example: Cross-classified chimpanzees with varying slopes

Continuous categories and the Gaussian process

Summary

Practice

14.Missing Data and Other Opportunities

Measurement error

Missing data

Summary

Practice

15.Horoscopes

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.

The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.

By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.

Web Resource

The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

(https://www.crcpress.com/Statistical-Rethinking-A-Bayesian-Course-with-Examples-in-R-and-Stan/McElreath/p/book/9781482253443)

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