practical applications. We survey these two methods in some detail and argue that Gibbs sampling methods can greatly reduce the …

computational strategy based on sampling the data, computing separate posterior distributions based on each sample, and then … combining these to get a consensus posterior inference. With hierarchical data structures, we perform cluster sampling into …

Monte Carlo (MC) approximation of the standard Gibbs procedure which uses sequential MC (SMC) importance sampling inside the … generic and easily implementable SMC approach known as Particle Efficient Importance Sampling (PEIS). By using SMC importance … sampling densities which are approximately fully globally adapted to the targeted density of the states, PEIS can substantially …

We consider Bayesian inference by importance sampling when the likelihood is analytically intractable but can be … unbiasedly estimated. We refer to this procedure as importance sampling squared (IS2), as we can often estimate the likelihood … itself by importance sampling. We provide a formal justification for importance sampling when working with an estimate of the …