Perfect simulation from unbiased simulation
George M. Leigh (1), Wen-Hsi Yang (2), Montana E. Wickens (1) and, Amanda R. Northrop (1) ((1) Queensland Department of Agriculture and, Fisheries, (2) The University of Queensland)

TL;DR
This paper demonstrates that unbiased simulation techniques can be converted into perfect simulation when coalescence of coupled Markov chains is practically guaranteed, enabling exact sampling from complex distributions with high iteration counts.
Contribution
The authors introduce a new approach that ensures perfect simulation by leveraging high iteration counts and develop an algorithm to efficiently generate multiple independent perfect samples.
Findings
Enabling perfect simulation with negligible probability of additional iterations.
Development of an efficient algorithm producing multiple independent perfect samples.
Application to Markov chains and high-dimensional normal distributions.
Abstract
We show that any application of the technique of unbiased simulation becomes perfect simulation when coalescence of the two coupled Markov chains can be practically assured in advance. This happens when a fixed number of iterations is high enough that the probability of needing any more to achieve coalescence is negligible; we suggest a value of . This finding enormously increases the range of problems for which perfect simulation, which exactly follows the target distribution, can be implemented. We design a new algorithm to make practical use of the high number of iterations by producing extra perfect sample points with little extra computational effort, at a cost of a small, controllable amount of serial correlation within sample sets of about 20 points. Different sample sets remain completely independent. The algorithm includes maximal coupling for continuous processes, to…
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Taxonomy
TopicsMarkov Chains and Monte Carlo Methods · Bayesian Methods and Mixture Models · Simulation Techniques and Applications
