Bayesian Software Packages

Nick Heard ()
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Nick Heard: Imperial College London

Chapter 6 in An Introduction to Bayesian Inference, Methods and Computation, 2021, pp 61-65 from Springer

Abstract: Abstract The research-level complexity of performing Bayesian inference with the statistical models typically encountered in practical decision problems can provide a barrier to these methods being widely deployed. To alleviate this problem, a number of probabilistic programming languages have been developed specifically to automate Bayesian inference. This text will focus on the language Stan, due to its widespread adoption and the depth of tutorial resources available. Brief details will also be given for two alternative libraries, PyMC and Edward. All three can be accessed through the general-purpose, interpreted programming languagePython Python. To illustrate the use of computer software packages in performing Bayesian inference, the following hypothetical statistical model will be used to provide a working example.

Date: 2021
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DOI: 10.1007/978-3-030-82808-0_6

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