Graham, Matthew M;
Livingstone, Samuel;
(2025)
rmcmc: Robust Markov chain Monte Carlo methods in R.
Journal of Open Source Software
, 10
(115)
p. 8594.
10.21105/joss.08594.
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Abstract
Generating samples from a probability distribution is a common requirement in many disciplines. In Bayesian inference, for example, the distribution of interest is the posterior over parameters of a model, given data assumed to be generated from the model. Expectations with respect to the posterior can be estimated using samples, allowing computation of posterior means, variances and other quantities of interest. Markov chain Monte Carlo (MCMC) methods are a general class of algorithms for approximately sampling from probability distributions. rmcmc is an R package providing implementations of MCMC methods for sampling from distributions on ℝ
| Type: | Article |
|---|---|
| Title: | rmcmc: Robust Markov chain Monte Carlo methods in R |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.21105/joss.08594 |
| Publisher version: | https://doi.org/10.21105/joss.08594 |
| Language: | English |
| Additional information: | Authors of papers retain copyright and release the work under a Creative Commons Attribution 4.0 International License (CC BY 4.0). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10216589 |
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