An Introduction to Bayesian Inference, Methods and Computation / by Nick Heard.

Format:

Book

Author/Creator:

Heard, Nicholas, author.

Series:

Mathematics and Statistics Series

Language:

English

Subjects (All):

Statistics.

Mathematical statistics--Data processing.

Mathematical statistics.

Quantitative research.

Bayesian Inference.

Statistics and Computing.

Data Analysis and Big Data.

Local Subjects:

Bayesian Inference.

Statistics and Computing.

Data Analysis and Big Data.

Physical Description:

1 online resource (176 pages)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches. .

Contents:

Uncertainty and Decisions

Prior and Likelihood Representation

Graphical Modeling

Parametric Models

Computational Inference

Bayesian Software Packages

Model choice

Linear Models

Nonparametric Models

Nonparametric Regression

Clustering and Latent Factor Models

Conjugate Parametric Models.

Notes:

Includes bibliographical references and index.

ISBN:

3-030-82808-5

OCLC:

1280127782