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Springer Bayesian Statistical Modeling with Stan, R, and Python

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Description

Master Bayesian statistical modeling with this practical guide from Springer. This book provides a comprehensive introduction to Stan, the most popular probabilistic programming language. It is structured into four parts to guide you through a professional modeling workflow. Start with the theoretical background of modeling and Bayesian inference to transform modeling from an art into an engineering process. The text covers the use of Stan, CmdStanR, and CmdStanPy, taking you from initial setup to basic regression analyses. You will explore various probability distributions, nonlinear models, and hierarchical modeling techniques. This resource is designed for those looking to apply advanced statistical methods using R and Python.

Key Features

Learn Bayesian statistical modeling using Stan, the most popular probabilistic programming language.

Follow a structured four-part guide that covers theoretical background and practical modeling workflows.

Gain hands-on experience with Stan, CmdStanR, and CmdStanPy for statistical analysis.

Study various probability distributions, nonlinear models, and hierarchical modeling techniques.

Transition from basic regression analyses to advanced Bayesian inference methods.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
25 January 2024
Listed Since
30 December 2023

Barcode

No barcode data available

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