An Introduction to Bayesian Inference, Methods and Computation (Hardcover, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	October 2021
First published:	2021
Authors:	Nick Heard
Dimensions:	235 x 155 x 17mm (L x W x T)
Format:	Hardcover
Pages:	169
Edition:	1st ed. 2021
ISBN-13:	978-3-03-082807-3
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software Books > Computing & IT > Applications of computing > Databases > General
LSN:	3-03-082807-7
Barcode:	9783030828073

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