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Population Monte Carlo algorithms [PDF]
Transactions of the Japanese Society for Artificial Intelligence, 2000 We give a cross-disciplinary survey on “population” Monte Carlo algorithms.In these algorithms, a set of “walkers” or “particles” is used as a representation of a high-dimensional vector. The computation is carried out by a random walk and split/deletion
Y. Iba
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Optimized Population Monte Carlo [PDF]
IEEE Transactions on Signal Processing, 2021 Adaptive importance sampling (AIS) methods are increasingly used for the approximation of distributions and related intractable integrals in the context of Bayesian inference.
V. Elvira, É. Chouzenoux
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Understanding population annealing Monte Carlo simulations. [PDF]
Physical Review E, 2021 Population annealing is a recent addition to the arsenal of the practitioner in computer simulations in statistical physics and it proves to deal well with systems with complex free-energy landscapes.
M. Weigel +3 more
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Population Quasi-Monte Carlo [PDF]
Journal of Computational and Graphical Statistics, 2020 Monte Carlo methods are widely used for approximating complicated, multidimensional integrals for Bayesian inference. Population Monte Carlo (PMC) is an important class of Monte Carlo methods, which adapts a population of proposals to generate weighted ...
Chaofan Huang +3 more
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Stochastic Gradient Population Monte Carlo
IEEE Signal Processing Letters, 2020 The population Monte Carlo (PMC) algorithm is a powerful adaptive importance sampling (AIS) methodology used for estimating expected values of random quantities w.r.t. some target probability distribution. At each iteration, a Markov transition kernel is
Yousef El-Laham, M. Bugallo
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Rare Events via Cross-Entropy Population Monte Carlo [PDF]
IEEE Signal Processing Letters, 2021 Rare events are events that happen with very low frequency. Estimating rare event probabilities using Monte Carlo techniques is computationally expensive, often to the point of intractability, and special methods are required. Importance sampling (IS) is
Caleb Miller, J. Corcoran, M. Schneider
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Minimum variance importance sampling via Population Monte Carlo [PDF]
ESAIM: Probability and Statistics, 2007 Summary: Variance reduction has always been a central issue in Monte Carlo experiments. Population Monte Carlo can be used to this effect, in that a mixture of importance functions, called a D-kernel, can be iteratively optimized to achieve the minimum asymptotic variance for a function of interest among all possible mixtures.
R. Douc, A. Guillin, J. Marin, C. Robert
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Journal of Computational and Graphical Statistics, 2004 Importance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions ...
Olivier Cappé +3 more
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Improving population Monte Carlo: Alternative weighting and resampling schemes [PDF]
Signal Processing, 2016 Population Monte Carlo (PMC) sampling methods are powerful tools for approximating distributions of static unknowns given a set of observations. These methods are iterative in nature: at each step they generate samples from a proposal distribution and assign them weights according to the importance sampling principle.
V. Elvira +3 more
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IEEE Transactions on Signal ProcessingImportance sampling (IS) is a powerful Monte Carlo (MC) technique for approximating intractable integrals, for instance in Bayesian inference. The performance of IS relies heavily on the appropriate choice of the so-called proposal distribution. Adaptive
Ali Mousavi, Víctor Elvira
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