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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

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Statistical Rethinking: A Bayesian Course with Examples in R and STAN ...

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.

Statistical Rethinking | Richard McElreath

Book sample: Chapters 1 and 2 (2MB PDF) Lectures and slides: * Winter 2022 materials (ongoing) * Winter 2019 materials Code and examples: * R package: rethinking (github repository) * R code examples from the book: code.txt * Book examples in Stan+tidyverse * brms + tidyverse conversion here * PyMC3 code examples: PyMC repository * NumPyro!

Statistical Rethinking | A Bayesian Course with Examples in R and STAN

Statistical Rethinking A Bayesian Course with Examples in R and STAN By Richard McElreath Edition 2nd Edition First Published 2020 eBook Published 15 March 2020 Pub. Location New York Imprint Chapman and Hall/CRC DOI Pages 612 eBook ISBN 9780429029608

Statistical rethinking with brms, ggplot2, and the tidyverse: Second ...

Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition version 0.4.0 A Solomon Kurz 2023-01-26 What and why This ebook is based on the second edition of Richard McElreath 's ( 2020a) text, Statistical rethinking: A Bayesian course with examples in R and Stan.

Statistical Rethinking - Google Books

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...

Statistical Rethinking 2023 - Lecture 16

Course: Intro music: Outline 00:00 Introduction 02:37 Oceanic spatial confounds 09:54 Gaussian processes 24:26 Oceanic Gaussian process 33:51 Pause 34:37 Phylogenetic regression 1:18:39 Summary and outlook

Statistical Rethinking 2023 - 02 - The Garden of Forking Data

Slides and other course materials at Pause music: Outline 00:00 Introduction 02:38 Generative model 08:42 The Garden of Forking Data 23:48 Bayesian updating 31:31 Probability 36:26 Testing 44:35 Pause 45:15 Infinite possibilities and the beta ...

Statistical Rethinking 2022 Lecture 02 - Bayesian Inference

Bayesian updating, sampling posterior distributions, computing posterior and prior predictive distributions Course materials: Intro music: Chapters: 00:00 Introduction 04:53 Garden of forking data 21:16 Globe tossing 42:38 Intermission 43:14 ...