[PDF] Top 20 Markov chain Monte Carlo on the GPU

... setting up and instantiating the kernel on the device. This means that your problem should also be sufficiently large to make it worth running on the GPU.[18] The performance benefit gained by stream processing is ... See full document

... A brief introduction to Markov chains The properties of the chain depend on P. The chain is irreducible if p ij pkq ¡ 0, for all i, j, and at least one k. aperiodic if all states have period 1: that ... See full document

... Fortunately, there are methods for suppressing random walks in Monte Carlo simulations, which we will discuss in the next chapter. 29.5 Gibbs sampling We introduced importance sampling, rejection sampling ... See full document

... sampling space. The chains run in parallel and interact with each other in var- ious ways (Gilks et al. (1994), Neal (1996), Liang and Wong (2001)). Hence, a population of chains explores the current state and defines ... See full document

... This introductory chapter describes the layout of this thesis, its primary contribu- tions, and introduces the terminology that will be used throughout the document. Chapter 2 presents the background research relevant to ... See full document

... MCMC does that by constructing a Markov Chain with stationary distribution  and simulating the chain... MCMC: Uniform Sampler[r] ... See full document

... a Markov chain, but tell little that cannot be seen at a glance at a time series plot like Figure ...a Markov chain started at different points, what we called the multistart heuristic ...the ... See full document

... multilevel Monte Carlo (MLMC) method was first introduced by Heinrich for the computation of high- dimensional, parameter-dependent integrals [41], and then rediscovered by Giles [30] in the context of ... See full document

... In this paper we address the problem of the prohibitively large computational cost of existing Markov chain Monte Carlo methods for large–scale applications with high dimensional parame- ter ... See full document

... – Multiple runs starting with different random number seed confirm MCMC sequences have converged to the target pdf.. Conclusions[r] ... See full document

... 2 Markov chains Markov chain Monte Carlo is a method to sample a given multivariate distri- bution π ∗ by constructing a suitable Markov chain with the property that its ... See full document

... We propose a new computationally efficient sampling scheme for Bayesian inference involving high dimensional probability distributions. Our method maps the original parameter space into a low-dimensional latent space, ... See full document

... An important task in machine learning and statis- tics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which ... See full document

... single Markov chain and β acts as an augmented state that is updated by moving up and down the temperature ...each chain and the number of exchanges at each ... See full document

... non-linear Markov Chain Monte Carlo (MCMC) methods for simulating from a probability measure ...Non-linear Markov kernels ...Self-Interacting Markov Chains (Del Moral & Miclo ... See full document

... 45 4.10 MCMC Posterior Sample Trace: Gompertz-Cucumber Model.. 45.[r] ... See full document

... As in Section 3.2, the approximations used for arriving at the test (18) imply that the stationary distribution of the chain need not be the exact posterior. However, we can expect to stay close to the true ... See full document

... Alexander Meyer-Gohde †§ Daniel Neuhoff ‡ Abstract We estimate ARMA (p,q) orders and parameters of the technology process in the neo- classical growth model using post war US GDP data and decisively reject the standard ... See full document

... some advantages. While MCMCpack does not currently support parallelization within the Monte Carlo loop, for some problems it is useful to perform embarrassingly parallel simuations, e.g., sampling from the ... See full document

... number of iterations for all SGMCMC algorithms. Figure 2 gives the trace plots for MCMC output of each algorithm for the case where d = 10 and N = 10 5 . Each of the SGMCMC algorithms is initialised with the same θ 0 and ... See full document