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Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems

Gallagher, K.; Charvin, Karl; Nielsen, Soren; Sambridge, Malcolm; Stephenson, John


We present an overview of Markov chain Monte Carlo, a sampling method for model inference and uncertainty quantification. We focus on the Bayesian approach to MCMC, which allows us to estimate the posterior distribution of model parameters, without needing to know the normalising constant in Bayes' theorem. Given an estimate of the posterior, we can then determine representative models (such as the expected model, and the maximum posterior probability model), the probability distributions for...[Show more]

CollectionsANU Research Publications
Date published: 2009
Type: Journal article
Source: Marine and Petroleum Geology
DOI: 10.1016/j.marpetgeo.2009.01.003


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