Results 11 to 20 of about 151,127 (170)

Sequential Quasi-Monte Carlo [PDF]

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2014
We derive and study SQMC (Sequential Quasi-Monte Carlo), a class of algorithms obtained by introducing QMC point sets in particle filtering. SQMC is related to, and may be seen as an extension of, the array-RQMC algorithm of L'Ecuyer et al. (2006).
Aistleitner, Andrieu, Andrieu, Andrieu, Andrieu, Arulampalam, Bach, Barvínek, Beaumont, Berard, Beskos, Blackwell, Blumer, Bocquet, Briers, Briers, Butz, Caflisch, Cappé, Carpenter, Carpenter, Carvalho, Ch, Chan, Chan, Chang, Chen, Chen, Chopin, Chopin, Chopin, Chopin, Chorin, Chorin, Cornebise, Cranley, Cvetanoska, Dacunha-Castelle, Del Moral, Del Moral, Del Moral, Del Moral, Del Moral, Del Moral, Devroye, Dick, Dick, Douc, Doucet, Doucet, Dutang, Fearnhead, Fearnhead, Fearnhead, Fearnhead, Ferguson, Gerber, Glasserman, Gordon, Gundersen, Guo, Götz, Hall, Hamilton, Heinrich, Hickernell, Hickernell, Hlawka, Hong, Hörmann, James, Johansen, Johansen, Julier, Kahn, Kantas, Kearns, Kitagawa, Koyama, Kuipers, Künsch, L'Ecuyer, L'Ecuyer, L'Ecuyer, L'Ecuyer, L'Ecuyer, L'Ecuyer, L'Ecuyer, Lacoste-Julien, Lemieux, Liu, Liu, Loh, Lopes, Lécot, Lécot, MacRae, Marin, Matoŭsek, McAlinn, Morzfeld, Muller, Muller, Murray, Murray, Møller, Neal, Niederreiter, Novak, Ormoneit, Ormoneit, Owen, Owen, Owen, Owen, Owen, Owen, Owen, Owen, Pitman, Pitt, Pitt, Pitt, Pollock, Poyiadjis, Poyiadjis, Raftery, Rebeschini, Regazzini, Reich, Robert, Rosenblatt, Rust, Sagan, Sherlock, Shinozuka, Skilling, Steigleder, Stein, Tuffin, van der Vaart, Vapnik, von Neumann, Wang, Wächter, Zhou, Ökten   +146 more
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Controlled Sequential Monte Carlo [PDF]

The Annals of Statistics, 2019
Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and
Bishop, Adrian N., Deligiannidis, George, Doucet, Arnaud, Heng, Jeremy   +3 more
core   +3 more sources

Lookahead Strategies for Sequential Monte Carlo [PDF]

Statistical Science, 2013
Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems.
Chen, Rong, Lin, Ming, Liu, Jun S.
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Nested Sequential Monte Carlo Methods [PDF]

, 2015
We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional.
Lindsten, Fredrik, Naesseth, Christian A., Schön, Thomas B.   +2 more
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Variational Sequential Monte Carlo

, 2018
Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member
Blei, David M., Linderman, Scott W., Naesseth, Christian A., Ranganath, Rajesh   +3 more
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Replica Conditional Sequential Monte Carlo [PDF]

, 2019
We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inference in non-linear non-Gaussian state-space models. Current state-of-the-art methods to address this problem rely on particle MCMC techniques and its variants, such as the iterated
Doucet, Arnaud, Shestopaloff, Alexander Y.   +1 more
core   +5 more sources

Sequential Monte Carlo for Graphical Models [PDF]

, 2014
We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically ...
Lindsten, Fredrik, Naesseth, Christian A., Schön, Thomas B.   +2 more
core   +4 more sources

Forest resampling for distributed sequential Monte Carlo [PDF]

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2014
This paper brings explicit considerations of distributed computing architectures and data structures into the rigorous design of Sequential Monte Carlo (SMC) methods.
Anthony Lee, Bertsekas, Bolić, Chopin, Chopin, Crisan, Cérou, Del Moral, Del Moral, Devroye, Doucet, Doucet, Hillis, Hockney, Isard, Kitagawa, Knuth, Kong, Lee, Liu, Liu, Mertens, Nick Whiteley, Pje\v{s}ivac-Grbović, Savage, Suchard, Vergé, Whiteley, Whiteley   +28 more
core   +7 more sources

Bayesian optimization using sequential Monte Carlo [PDF]

, 2011
We consider the problem of optimizing a real-valued continuous function $f$ using a Bayesian approach, where the evaluations of $f$ are chosen sequentially by combining prior information about $f$, which is described by a random process model, and past ...
D.R. Jones, N. Chopin, P. Moral Del, R. Benassi, R. Gramacy   +4 more
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Critic Sequential Monte Carlo

CoRR, 2022
We introduce CriticSMC, a new algorithm for planning as inference built from a composition of sequential Monte Carlo with learned Soft-Q function heuristic factors. These heuristic factors, obtained from parametric approximations of the marginal likelihood ahead, more effectively guide SMC towards the desired target distribution, which is particularly ...
Vasileios Lioutas, Jonathan Wilder Lavington, Justice Sefas, Matthew Niedoba, Yunpeng Liu 0007, Berend Zwartsenberg, Setareh Dabiri, Frank Wood, Adam Scibior   +8 more
openaire   +3 more sources