
Stability of Conditional Sequential Monte Carlo
The particle Gibbs (PG) sampler is a Markov Chain Monte Carlo (MCMC) alg...
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A splitting method to reduce MCMC variance
We explore whether splitting and killing methods can improve the accurac...
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Increasing the efficiency of Sequential Monte Carlo samplers through the use of approximately optimal Lkernels
By facilitating the generation of samples from arbitrary probability dis...
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Making meanestimation more efficient using an MCMC trace variance approach: DynaMITE
The MarkovChain MonteCarlo (MCMC) method has been used widely in the l...
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Variance Estimation in Adaptive Sequential Monte Carlo
Sequential Monte Carlo (SMC) methods represent a classical set of techni...
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Optimal input potential functions in the interacting particle system method
The assessment of the probability of a rare event with a naive MonteCar...
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Revisiting the GelmanRubin Diagnostic
Gelman and Rubin's (1992) convergence diagnostic is one of the most popu...
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Wastefree Sequential Monte Carlo
A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, wastefree SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm in practice. We develop in particular a method to estimate, from a single run of the algorithm, the asymptotic variance of any particle estimate. We show empirically, through a range of numerical examples, that wastefree SMC tends to outperform standard SMC samplers, and especially so in situations where the mixing of the considered MCMC kernels decreases across iterations (as in tempering or rare event problems).
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