Bayesian Statistical Modeling With Stan, R, and Python
English | 2023 | ISBN: 9811947546 | 395 Pages | PDF EPUB (True) | 41 MB
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.
Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
Fikper
2n67n.B.S.M.W.S.R.a.P.rar.html
Rapidgator
2n67n.B.S.M.W.S.R.a.P.rar.html
NitroFlare
2n67n.B.S.M.W.S.R.a.P.rar
Uploadgig
2n67n.B.S.M.W.S.R.a.P.rar
Links are Interchangeable – No Password – Single Extraction