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Ergonomic and Reliable Bayesian Inference with Adaptive Markov Chain Monte Carlo (2020)

Vihola, M. (2020). Ergonomic and Reliable Bayesian Inference with Adaptive Markov Chain Monte Carlo. In N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, & J. L. Teugels (Eds.), Wiley StatsRef : Statistics Reference Online (pp. 1-12). John Wiley & Sons. https://doi.org/10.1002/9781118445112.stat08286

JYU authors or editors

Publication details

All authors or editors: Vihola, Matti

Parent publication: Wiley StatsRef : Statistics Reference Online

Parent publication editors: Balakrishnan, N.; Colton, T.; Everitt, B.; Piegorsch, W.; Ruggeri, F.; Teugels, J. L.

eISBN: 978-1-118-44511-2

Publication year: 2020

Pages range: 1-12

Publisher: John Wiley & Sons

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1002/9781118445112.stat08286

Publication open access: Not open

Publication channel open access:


Adaptive Markov chain Monte Carlo (MCMC) methods provide an ergonomic way to perform Bayesian inference, imposing mild modeling constraints and requiring little user specification. The aim of this section is to provide a practical introduction to selected set of adaptive MCMC methods and to suggest guidelines for choosing appropriate methods for certain classes of models. We consider simple unimodal targets with random-walk-based methods, multimodal target distributions with parallel tempering, and Bayesian hidden Markov models using particle MCMC. The section is complemented by an easy-to-use open-source implementation of the presented methods in Julia, with examples.

Keywords: Markov chains; Monte Carlo methods; Bayesian analysis; methods

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 2

Last updated on 2021-07-07 at 21:31