In this paper, we study the asymptotic variance of sample path averages for inhomogeneous Markov chains that evolve alternatingly according to two different 7-reversible Markov transition kernels P… Expand

Bayesian inference of Gibbs random fields (GRFs) is often referred to as a doubly intractable problem, since the likelihood function is intractable. The exploration of the posterior distribution of… Expand

We introduce a pseudo-posterior distribution that approximates the likelihood function in the posterior distribution of Exponential random graph models and discuss the computational and statistical efficiency that result from this approach.Expand

In this paper we study asymptotic properties of different data-augmentation-type Markov chain Monte Carlo algorithms sampling from mixture models comprising discrete as well as continuous random variables and discuss and compare different algorithms based on this scheme.Expand

Light and Widely Applicable (LWA-) MCMC is a novel approximation of the Metropolis‐ Hastings kernel targeting a posterior distribution defined on a large number of observations. Inspired by… Expand

Exponential Random Graph models are an important tool in network analysis for describing complicated dependency structures. However, Bayesian parameter estimation for these models is extremely… Expand