An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

By Jesper Møller, Anthony N. Pettitt, R. Reeves, Kasper K. Berthelsen. 2006

410 citations recorded by the source corpus.

Subfields: Condensed Matter Physics, Mathematical Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1e918d01e7df34379525bd008caaeb547b878aa9

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In Condensed Matter Physics

In Mathematical Physics

In Statistics and Probability

Citations in the corpus

Robust generalised Bayesian inference for intractable likelihoods (2021, neutral): “…al., 2018; Tavaré et al., 1997), (2) auxiliary variable MCMC (such as the exchange algorithm and pseudo-marginal MCMC; Andrieu & Roberts, 2009; M ⊘ ller et al., 2006; Murray et al., 2006; Park & Haran, 2018), and (3) approximate likelihood methods (such as pseudo-likelihood and composite…”
Edinburgh Research Explorer Continuous Relaxations for Discrete Hamiltonian Monte Carlo (neutral): “This importance trick is well known in the statistics literature, e.g., [14].”
Markov chain Monte Carlo for a Spatial-Temporal Autologistic Regression Model (2008, methodology): “Recently Møller et al. (2006) proposed an exact method for sampling from the posterior of an autologistic model by using an auxiliary random field from a particular distribution and selecting a specific proposal distribution.”
Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015, background): “Another substantial and still unexplored area where adaptive algorithms are applied for very high dimensional and multimodal problems is model and variable selection (Nott and Kohn (2005); Richardson et al. (2010);”
Modern Statistics for Spatial Point Processes * (2007, methodology): “Based on perfect simulation (section 9.3) and auxiliary variable MCMC methods (Møller et al., 2006), progress on Bayesian inference for Markov point processes has been made in Berthelsen & Møller (2003, 2004, 2006, 2007).”
Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods (2014, methodology): “From a Bayesian perspective, simulating from the likelihood has also played an inﬂuential role in several approaches, for example, the auxiliary variable method of Møller et al. (2006), that was subsequently extended by Murray et al. (2006) to the exchange algorithm.”
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks (2012, methodology): “There are several different classes of approaches for avoiding this: pseudo-likelihood (Besag 1975), variational approximations (Murray and Ghahramani 2004), Monte Carlo approximations (Geyer and Thompson 1992; Green and Richardson 2002; Atchadé, Lartillot, and Robert 2008), auxiliary variable appro”
Approximate Bayesian computational methods (2011, methodology): “proposed by Møller et al. (2006). However the dependency”
Bayesian Inference and Model Assessment for Spatial Point Patterns Using Posterior Predictive Samples (2017, methodology): “To avoid using the pseudolikelihood, Møller et al. (2006) discuss an auxiliary variable approach in which the auxiliary variable comes from the same state space as the point pattern.”
Bayesian computation: a summary of the current state, and samples backwards and forwards (2015, methodology): “Examples of this kind abound in Markov random ﬁelds models, as for instance for the Ising model (Murray et al. 2006b; Møller et al. 2006).”
MCMC Using Hamiltonian Dynamics (2011, methodology): “For instance, in [33], the method is illustrated on the well-known Ising model which was proposed as a main application in Propp and Wilson’s landmark paper [48], which is a “must” for any tourist of the magic land of perfect sampling.”