May 16, 2008 · Markov chain Monte Carlo (MCMC) is an important computational technique for generating samples from non-standard probability distributions.

In this work we develop an independence sampler based MCMC method for the Bayesian inferences of functions. We represent the proposal distribution as a mixture ...

In this paper, we propose two new adaptive MCMC algorithms, which we call Adaptive Independence Samplers (AIS). The first approach, which can be considered ...

Markov chain Monte Carlo (MCMC) is an important computational technique for generating samples from non-standard probability distributions.

Abstract. Markov chainMonte Carlo (MCMC) is an importantcomputational technique for generating samples fromnon-standard probability distributions.

In this paper, we propose two new adaptive MCMC al- gorithms, which we call Adaptive Independence Samplers. (AIS). The first approach, which can be ...

Two new adaptive MCMC algorithms based on the Independent Metropolis–Hastings algorithm are proposed, one of which provides a general technique for deriving ...

In this paper, we propose two new adaptive MCMC algorithms based on the Independent Metropolis–Hastings algorithm. In the first, we adjust the proposal to ...

The figure compares the three Metropolis–Hastings algorithms: independence sampler. (line), random walk (dotted) and adaptive independent sampler (dashed).

The Bayesian Adaptive Independence Sampler (BAIS) [5] is a population MCMC algorithm consisting of N independence samplers run in parallel, all with a ...