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 . WebNov 23, 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 …
Optimal thinning of mcmc output
Did you know?
WebP MCMC output Representative Subset (θ i)n =1 (θ i) i∈S Desiderata: Fix problems with MCMC (automatic identification of burn-in; mitigation of poor mixing; number of points … WebThe 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. ... "Optimal thinning of MCMC output," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1059-1081, September. Handle ...
WebAug 3, 2024 · For example, correlated samples from a posterior distribution are obtained using a MCMC algorithm and stored in the matrix smpl, and the corresponding gradients of the log-posterior are stored in another matrix grad. One can then perform Stein Thinning to obtain a subset of 40 sample points by running the following code: 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.
WebJun 17, 2011 · We thus compare four MCMC sampling procedures: (1) with A = 6, unthinned; (2) with A = 6, thinning ×10; (3) with A = 1, unthinned; and (4) with A = 1, thinning ×100. We implemented each procedure for chains of length 10 4, 10 5 and 10 6 (before thinning). WebJan 31, 2024 · Stein thinning is a promising algorithm proposed by (Riabiz et al., 2024) for post-processing outputs of Markov chain Monte Carlo (MCMC). The main principle is to greedily minimize the kernelized Stein discrepancy (KSD), which only requires the gradient of the log-target distribution, and is thus well-suited for Bayesian inference.The main …
Webthinning frequency t, leading to an approximation of the form 1 b(n b)=tc b(nX b)=tc i=1 (X b+it): (3) Here brcdenotes the integer part of r. This corresponds to a set of indices ˇin (2) …
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 that are produced. Typically a number of the initial states are attributed to "burn in" and removed, whilst the remainder of the chain is "thinned" if compression is also required. In this paper … china yellow pagesWebThe inefficiency of thinning MCMC output has been known since the early 1990's, long before MCMC appeared in ecological publications. 4. We discuss the background and prevalence of thinning, illustrate its consequences, discuss circumstances when it might be regarded as a reasonable option and recommend against routine thinning of chains … grand banks 54 2020 priceWebApr 3, 2024 · Optimal thinning of MCMC output; Optimal thinning of MCMC output. SWIETACH P. Original publication. DOI. 10.1111/rssb.12503. Type. Journal article. … china years animalsWebThese 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. china yellow sea wetland museumWebNov 23, 2024 · 23 Nov 2024, 07:42 (modified: 10 Jan 2024, 17:10) AABI2024 Readers: Everyone Abstract: 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. grand banks 54 eastbay sxWebIn 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. china yellow mountains bridgeWebJan 10, 2024 · When used as a Markov Chain Monte Carlo (MCMC) algorithm, we show that the ODE approximation achieves a 2-Wasserstein error of ε in 𝒪 (d^1/3/ε^2/3) steps under the standard smoothness and strong convexity assumptions on the target distribution. china yellow river flood