An Introduction to Bayesian Inference, Methods and Computation

✍ Scribed by Nick Heard

Publisher: Springer
Year: 2021
Tongue: English
Leaves: 181
Edition: 1st ed. 2021
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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