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Springer - An Introduction to Bayesian Inference and Methods

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Description

Master the fundamentals of Bayesian statistical methods with this accessible guide from Springer. These lecture notes offer a rapid introduction to the core philosophy and principles of Bayesian inference. You will learn the reasoning behind prior and likelihood model construction, moving from foundational concepts to advanced topics like Gaussian processes, nonparametrics, and latent factor models. This book bridges the gap between theory and practice by integrating Python and Stan computer code samples directly into the text. You will gain practical experience applying advanced modeling techniques and learn essential methods for assessing model fit. It is an ideal resource for anyone looking to implement modern Bayesian computational methods through hands-on coding and mathematical instruction.

Key Features

This Springer publication provides a rapid and accessible introduction to Bayesian statistical methods and principles.

Learn to construct prior and likelihood models through a clear explanation of Bayesian reasoning and philosophy.

Cover advanced topics including nonparametrics, latent factor models, and Gaussian processes.

Apply advanced modeling techniques using integrated Python and Stan computer code samples provided in the text.

Gain practical skills in assessing model fit to ensure the accuracy of your statistical computations.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
18 October 2021
Listed Since
26 June 2021

Barcode

No barcode data available

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