# Bayesian

# Doing Meta-Analysis in R

## by Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert

This is a guide on how to conduct Meta-Analyses in R. […] Welcome to the online version of “Doing Meta-Analysis with R: A Hands-On Guide”. This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced, but highly relevant topics such as network meta-analysis, multi-/three-level meta-analyses, Bayesian … Read more →

# A Bayesian Introduction to Fish Population Analysis

## by Joseph E. Hightower

This book is intended to be a bridge between traditional fisheries analytical methods and Bayesian statistics. It is a hands-on introduction to models for estimating abundance, survival, growth, recruitment, and population trends. […] This book is based in large part on material I developed while teaching (1991-2014) at NC State University. My hope is that the book will be a bridge between traditional fisheries analytical methods and Bayesian approaches that offer many advantages in ecological modeling. The book might be useful as an upper-level undergraduate or early graduate text, or for … Read more →

# Personal Notes: A Student’s Guide to Bayesian Statistics

## by Peter Baumgartner

Peter Baumgartner This Quarto book collects my personal notes, trials and exercises of the A Student’s Guide to Bayesian Statistics by Ben … Read more →

# Probability & Bayesian Modeling

## by Peter Baumgartner

Peter Baumgartner This Quarto book collects my personal notes, trials and exercises of Probability & Bayesian Modeling by Jim Albert & Jingchen … Read more →

# Bayesian Statistics the Fun Way

## by Peter Baumgartner

Peter Baumgartner This Quarto book collects my personal notes, trials and exercises of the Bayesian Statistics the Fun Way: Understanding Statistics and Probability With Star Wars, LEGO, and Rubber Ducks by Will Kurt (Kurt 2019). I am not an expert in statistics. During my study in sociology back in 1970er I had only rudimentary learned about frequentist statistics. There weren’t computer only via time-sharing and keypunching for card-to-tape converter available. This was painstaking and not motivating because it had not much practical values. 10 years later I worked sometimes with SPSS but … Read more →

# Regression and Analysis of Variance

## by Trevor Hefley

Course notes for Regression and Analysis of Variance (STAT 705) at Kansas State University for Summer 2023 […] This document contains the course notes for Regression and Analysis of Variance at Kansas State University (STAT 705). During the semester we will cover the basics such as regression and ANOVA modeling, parameter estimation, model checking, inference, and prediction. We may also cover modern topics such as regularization, random effects, generalized linear models, machine learning approaches, and Bayesian regression and … Read more →

# Applied longitudinal data analysis in brms and the tidyverse

## by A Solomon Kurz

This project is a reworking of Singer and Willett’s classic (2003) text within a contemporary Bayesian framework with emphasis of the brms and tidyverse packages within the R computational framework. […] This project is based on Singer and Willett’s classic (2003) text, Applied longitudinal data analysis: Modeling change and event occurrence. You can download the data used in the text at http://www.bristol.ac.uk/cmm/learning/support/singer-willett.html and find a wealth of ideas on how to fit the models in the text at https://stats.idre.ucla.edu/other/examples/alda/. My contributions show … Read more →

# Applied Bayesian Modeling and Prediction

## by Trevor Hefley

Course notes for Applied Bayesian Modeling and Prediction (STAT 768) at Kansas State University for Spring 2023 semester […] This document contains the course notes for Applied Bayesian Modeling and Prediction (STAT 768) at Kansas State University. During the semester we will cover the basics such as the Bayesian model development, implementation, checking, and inference/prediction. We will focus on formulating and implementing bespoke Bayesian models that are tailored to answer scientific questions or applied problems ranging from environmental management to … Read more →

# Bayesian Linear Regression Tutorial

## by Xiang Chen, Valentina Arputhasamy, Daniel Zhou, Sudipto Banerjee

This is a first tutorial for Bayesian Linear Regression assembled in book form. […] This is a tutorial for Bayesian Linear Regression assembled in book … Read more →

# A Brief Introduction to Bayesian Inference

## by Johnny van Doorn

A brief introduction to Bayesian concepts, based on a beer-tasting experiment. […] This book is still a work in progress If you encounter any errors/issues, you can reach me here. This booklet offers an introduction to Bayesian inference. We look at how different models make different claims about a parameter, how they learn from observed data, and how we can compare these models to each other. We illustrate these ideas through an informal beer-tasting experiment conducted at the University of Amsterdam.1 A key concept in Bayesian inference is predictive quality: how well did a model, or … Read more →

# Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition

## by A Solomon Kurz

This book is an attempt to re-express the code in the second edition of McElreath’s textbook, ‘Statistical rethinking.’ His models are re-fit in brms, plots are redone with ggplot2, and the general data wrangling code predominantly follows the tidyverse style. […] 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. My contributions show how to fit the models he covered with Paul Bürkner’s brms package (Bürkner, 2017, 2018, 2022j), which makes it easy to fit Bayesian regression models in R (R … Read more →

# Doing Bayesian Data Analysis in brms and the tidyverse

## by A Solomon Kurz

This project is an attempt to re-express the code in Kruschke’s (2015) textbook. His models are re-fit in brms, plots are redone with ggplot2, and the general data wrangling code predominantly follows the tidyverse style. […] Kruschke began his text with “This book explains how to actually do Bayesian data analysis, by real people (like you), for realistic data (like yours).” In the same way, this project is designed to help those real people do Bayesian data analysis. My contribution is converting Kruschke’s JAGS and Stan code for use in Bürkner’s brms package (Bürkner, 2017, 2018, 2022g), … Read more →

# Statistical rethinking with brms, ggplot2, and the tidyverse

## by A Solomon Kurz

This project is an attempt to re-express the code in McElreath’s textbook. His models are re-fit in brms, plots are redone with ggplot2, and the general data wrangling code predominantly follows the tidyverse style. […] I love McElreath’s (2015) Statistical rethinking text. It’s the entry-level textbook for applied researchers I spent years looking for. McElreath’s freely-available lectures on the book are really great, too. However, I prefer using Bürkner’s brms package (Bürkner, 2017, 2018, 2022i) when doing Bayesian regression in R. It’s just spectacular. I also prefer plotting with … Read more →

# Recoding Introduction to Mediation, Moderation, and Conditional Process Analysis

## by A Solomon Kurz

This ebook is an effort to connect Hayes’s conditional process analysis work with the Bayesian paradigm. Herein I refit his models with my favorite R package for Bayesian regression, Bürkner’s brms, and use the tidyverse for data manipulation and plotting. […] Andrew Hayes’s (2018) text, Introduction to mediation, moderation, and conditional process analysis: A regression-based approach, has become a staple in social science graduate education. Hayes’s work has been from a frequentist OLS perspective. This book is an effort to connect his work with the Bayesian paradigm. Herein I refit his … Read more →

# Bayesian Linear Regression

## by Xiang Chen, Dr. Sudipto Banerjee

This is a tutorial for Bayesian Linear Regressione. […] Theorem 1.1 (Bayes’ theorem) For events (A, B) and (P(B) \neq 0), we have [P(A\mid B) = \frac{P(B \mid A) P(A)}{P(B)}] We denote (U) as unknown parameters and (K) as known parameters. We call (P(U)) prior and (P(K|U)) likelihood. The Bayes’ theorem gives us the posterior distribution of unknown parameters given the known parameters [ P(U \mid K) \propto P(U) \cdot P(K \mid U)] Let (K = \left{y_{n \times 1}, X_{n \times p} \right}) and assume (y \sim N\left( X \beta, \sigma^{2} V\right)), where (V) is known and … Read more →

# Introduction to Bayesian Econometrics

## by Andrés Ramírez-Hassan

The subject of this textbook is Bayesian regression analysis, and its main aim is to provide introductory level theory foundation, and facilitate applicability of Bayesian inference. […] Since late 90’s Bayesian inference has gained a lot of popularity among researchers due to the computational revolution and availability of algorithms to solve complex integrals. However, many researchers, students and practitioners still lack understanding and application of this inferential approach. The main reason is the requirement of good programming skills. Introduction to Bayesian econometrics: A … Read more →

# Multilevel Regression and Poststratification Case Studies

## by Juan Lopez-Martin, Justin H. Phillips, and Andrew Gelman

Introduction to Bayesian Multilevel Modeling and Poststratification using rstanarm, brms, and Stan […] The following case studies intend to introduce users to Multilevel Modeling and Poststratification (MRP) and some of its extensions, providing reusable code and clear explanations. The first section1 presents MRP, a statistical technique that allows to estimate subnational estimates from national surveys while adjusting for nonrepresentativeness. The second chapter extends MRP to overcome the limitation of only using variables included in the census. The last chapter develops a new … Read more →

# An Introduction to Bayesian Reasoning and Methods

## by Kevin Ross

This textbook presents an introduction to Bayesian reasoning and methods […] Statistics is the science of learning from data. Statistics involves We will assume some familiarity with many of these aspects, and we will focus on the items in italics. That is, we will focus on statistical inference, the process of using data analysis to draw conclusions about a population or process beyond the existing data. “Traditional” hypothesis tests and confidence intervals that you are familiar with are components of “frequestist” statistics. This book will introduce aspects of “Bayesian” statistics. We … Read more →

# R 로 하는 Mixed Model

## by Michael Clark m-clark.github.io Translator : 김설기

This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mixed models, Bayesian approaches, and realms beyond. […] Michael Clark m-clark.github.io Translator : 김설기 … Read more →

# A Quick Introduction to bbsBayes

## by Adam C. Smith, and Brandon P.M. Edwards

This is a document to support a ~2hr workshop. […] This is a 2-hour introductory workshop/demonstration of the R-package bbsBayes (https://github.com/BrandonEdwards/bbsBayes). This package allows anyone to apply the hierarchical Bayesian models used to estimate status and trends from the North American Breeding Bird Survey. The package also lets the user generate a suite of alternative metrics using the existing model output from the annual CWS analyses. Everyone is welcome! Some familiarity working with R is required if you’d also like to run the code yourself during the workshop, and the … Read more →

# Efficient R programming

## by Colin Gillespie, Robin Lovelace

Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. […] This is the online version of the O’Reilly book: Efficient R programming. Pull requests and general comments are welcome. Get a hard copy from: Amazon (UK), Amazon (USA), O’Reilly Colin Gillespie is Senior Lecturer (Associate Professor) at Newcastle University, UK. He is an Executive Editor of the R Journal, with research interests including high performance statistical computing and Bayesian statistics. Colin founded the … Read more →

# EMMERG Spatial

## by Gabriel Carrasco

Working notes […] Quantify the effects of contextual variables by coupling spatial and temporal structures in a Bayesian model framework. The model will then used to produce out-of-sample predicted probabilities of exceeding county-specific outbreak thresholds to develop early warning information to predict the risk of emerging drug … Read more →

# Recoding Introduction to Mediation, Moderation, and Conditional Process Analysis

## by A Solomon Kurz

This project is an effort to connect his Hayes’s conditional process analysis work with the Bayesian paradigm. Herein I refit his models with my favorite R package for Bayesian regression, Bürkner’s brms, and use the tidyverse for data manipulation and plotting. […] Andrew Hayes’s Introduction to Mediation, Moderation, and Conditional Process Analysis text, the second edition of which just came out, has become a staple in social science graduate education. Both editions of his text have been from a frequentist OLS perspective. This project is an effort to connect his work with the Bayesian … Read more →

# Course Handouts for Bayesian Data Analysis Class

## by Mark Lai

This is a collection of my course handouts for PSYC 621 class in the 2019 Spring semester. Please contact me [mailto:hokchiol@usc.edu] for any errors (as I’m sure there are plenty of them). […] This is a collection of my course handouts for PSYC 621 class. The materials are based on the book by McElreath (2016), the brms package (Bürkner 2017), and the STAN language. Please contact me for any errors (as I’m sure there are plenty of them). Bürkner, Paul-Christian. 2017. “brms: An R Package for Bayesian Multilevel Models Using Stan.” Journal of Statistical Software 80 (1): 1–28. … Read more →

# Bayesian Hierarchical Models in Ecology

## by Steve Midway

This is a book that is build on lectures from a course of the same name. […] Welcome to Bayesian Hierarchical Models in Ecology. This is an ebook that is also serving as the course materials for a graduate class of the same name. There will be numerous and on-going changes to this book, so please check back. And don’t hesistate to email me if you have questions, comments, or for anything else. To start, let’s calrify the title of this text—it should be Hierarchical Models in Ecology Using Bayesian Inference. A Bayesian Hierarchical Model is more a term of convenience than accuracy, as … Read more →

# Graphical & Latent Variable Modeling

## by Michael Clark m-clark.github.io

This document focuses on structural equation modeling. It is conceptually based, and tries to generalize beyond the standard SEM treatment. It includes special emphasis on the lavaan package. Topics include: graphical models, including path analysis, bayesian networks, and network analysis, mediation, moderation, latent variable models, including principal components analysis and ‘factor analysis’, measurement models, structural equation models, mixture models, growth curves, item response theory, Bayesian nonparametric techniques, latent dirichlet allocation, and more. Read more →

# Bayesian Basics

## by Michael Clark m-clark.github.io

This document provides an introduction to Bayesian data analysis. It is conceptual in nature, but uses the probabilistic programming language Stan for demonstration (and its implementation in R via rstan). From elementary examples, guidance is provided for data preparation, efficient modeling, diagnostics, and more. […] Michael Clark m-clark.github.io … Read more →

# Mixed Models with R

## by Michael Clark m-clark.github.io

This is an introduction to using mixed models in R. It covers the most common techniques employed, with demonstration primarily via the lme4 package. Discussion includes extensions into generalized mixed models, Bayesian approaches, and realms beyond. […] Michael Clark m-clark.github.io … Read more →