Data Analysis Using Regression And Multilevel Hierarchical Models Github
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Bayesian Statistical Modeling with Stan, R, and Python
This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines. Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.
Data Analysis Using Regression and Multilevel/Hierarchical Models
Author: Andrew Gelman
language: en
Publisher: Cambridge University Press
Release Date: 2007
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Open Source Systems
This open access book constitutes the refereed proceedings of the 15th IFIP WG 2.13 International Conference on Open Source Systems, OSS 2019, held in Montreal, Quebec, Canada, in May 2019. The 10 revised full papers and 5 short papers presented were carefully reviewed and selected from 35 submissions. The papers cover a wide range of topics in the field of free/libre open source software (FLOSS) and are organized in the following thematic sections: mining OSS data; organizational aspects of FLOSS projects; FLOSS adoption; FLOSS cost and licenses; and FLOSS education and training.