WebThese include discrepancy minimisation, gradient flows and control functionals—all of which have the potential to deliver faster convergence than a Monte Carlo method. In this talk we will see how ideas from discrepancy minimisation can be applied to the problem of optimal thinning of MCMC output. WebIn this paper we propose a novel method, called Stein Thinning, that selects an indexset π, of specified cardinality m, such that the associated empirical approximation is closeto optimal. The method is designed to ensure that (2) is a consistent approximation of P .

Optimal Thinning of MCMC Output - arxiv.org

WebIn this paper we consider the problem of retrospectively selecting a subset of states, of fixed cardinality, from the sample path such that the approximation provided by their empirical distribution is close to optimal. WebMay 8, 2024 · Optimal Thinning of MCMC Output. The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal … how much are save the dates

On thinning of chains in MCMC - Link - 2012 - besjournals

WebFeb 3, 2024 · Organisation. The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations that are produced. Here we consider the problem of retrospectively selecting a subset of states, of fixed cardinality, from the sample path such that the … WebMCMC output. q For Raftery and Lewis diagnostic, the target quantile to be estimated r For Raftery and Lewis diagnostic, the required precision. s For Raftery and Lewis diagnostic, the probability of obtaining an estimate in the interval (q-r, q+r). quantiles Vector of quantiles to print when calculating summary statistics for MCMC output. WebMay 8, 2024 · The use of heuristics to assess the convergence and compress the output of Markov chain Monte Carlo can be sub-optimal in terms of the empirical approximations … how much are scatpacks