A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants

By F. Liang. 2010

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Robust generalised Bayesian inference for intractable likelihoods (2021, unknown): “, 2018; Cherief-Abdellatif and Alquier, 2020; Frazier, 2020), (2) auxiliary variable MCMC (such as the exchange algorithm and pseudo-marginal MCMC; Møller et al., 2006; Murray et al., 2006; Andrieu and Roberts, 2009; Liang, 2010; Lyne et al., 2015; Doucet et al., 2015; Andrieu et al., 2020), and (3)”
A Structural Model of Segregation in Social Networks (2010, methodology): “Adapting the suggestion in Liang (2010), define P θ′ (g′|g) as the transition probability of a Markov chain that generates g′ with R Metropolis-Hastings updates of the algorithm, starting at the observed network g and using the proposed parameter θ′.”
On Nesting Monte Carlo Estimators (2017, background): “A common class of nested expectations is doubly-intractable inference problems (Murray et al., 2006; Liang, 2010), where the likelihood is only known up to a parameter-dependent normalizing constant.”