An Introduction to Bayesian Inference, Methods and Computation

1. Auflage 2021
ISBN: 978-3-030-82807-3
Verlag: Springer International Publishing

Buch, Englisch, 169 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 448 g

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.

Zielgruppe

Graduate

Autoren/Hrsg.

Heard, Nick

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz

Inhaltsverzeichnis

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.

Über Autor(innen)

Professor Nick Heard received his PhD degree from the Department of Mathematics at Imperial College London in 2001 and currently holds the position of Chair in Statistics at Imperial. His research interests include developing statistical models for cyber-security applications, finding community structure in large dynamic networks, clustering and changepoint analysis, in each case using computational Bayesian methods.