A backward particle interpretation of Feynman-Kac formulae
ESAIM: M2AN, 44 5 (2010) 947-975
Published online: 2010-08-26
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
Variance estimation for sequential Monte Carlo algorithms: A backward sampling approach
Yazid Janati El Idrissi, Sylvain Le Corff and Yohan PetetinBernoulli 30 (2) (2024)
https://doi.org/10.3150/23-BEJ1586
Unbiased and multilevel methods for a class of diffusions partially observed via marked point processes
Miguel Alvarez, Ajay Jasra and Hamza RuzayqatStatistics and Computing 34 (6) (2024)
https://doi.org/10.1007/s11222-024-10518-1
Fast and Numerically Stable Particle-Based Online Additive Smoothing: The AdaSmooth Algorithm
Alessandro Mastrototaro, Jimmy Olsson and Johan AlenlövJournal of the American Statistical Association 119 (545) 356 (2024)
https://doi.org/10.1080/01621459.2022.2118602
Backward Importance Sampling for Online Estimation of State Space Models
Alice Martin, Marie-Pierre Étienne, Pierre Gloaguen, Sylvain Le Corff and Jimmy OlssonJournal of Computational and Graphical Statistics 32 (4) 1447 (2023)
https://doi.org/10.1080/10618600.2023.2174125
On backward smoothing algorithms
Hai-Dang Dau and Nicolas ChopinThe Annals of Statistics 51 (5) (2023)
https://doi.org/10.1214/23-AOS2324
A pseudo-marginal sequential Monte Carlo online smoothing algorithm
Pierre Gloaguen, Sylvain Le Corff and Jimmy OlssonBernoulli 28 (4) (2022)
https://doi.org/10.3150/21-BEJ1431
Unbiased estimation of the gradient of the log-likelihood for a class of continuous-time state-space models
Marco Ballesio and Ajay JasraMonte Carlo Methods and Applications 28 (1) 61 (2022)
https://doi.org/10.1515/mcma-2022-2105
Online Particle Smoothing With Application to Map-Matching
Samuel Duffield and Sumeetpal SinghIEEE Transactions on Signal Processing 70 497 (2022)
https://doi.org/10.1109/TSP.2022.3141259
Bias of Particle Approximations to Optimal Filter Derivative
Vladislav Z. B. Tadić and Arnaud DoucetSIAM Journal on Control and Optimization 59 (1) 727 (2021)
https://doi.org/10.1137/18M1217024
Score-Based Parameter Estimation for a Class of Continuous-Time State Space Models
Alexandros Beskos, Dan Crisan, Ajay Jasra, Nikolas Kantas and Hamza RuzayqatSIAM Journal on Scientific Computing 43 (4) A2555 (2021)
https://doi.org/10.1137/20M1362942
Particle-based online estimation of tangent filters with application to parameter estimation in nonlinear state-space models
Jimmy Olsson and Johan Westerborn AlenlövAnnals of the Institute of Statistical Mathematics 72 (2) 545 (2020)
https://doi.org/10.1007/s10463-018-0698-1
Particle-Based Adaptive-Lag Online Marginal Smoothing in General State-Space Models
Johan Alenlov and Jimmy OlssonIEEE Transactions on Signal Processing 67 (21) 5571 (2019)
https://doi.org/10.1109/TSP.2019.2941066
On Large Lag Smoothing for Hidden Markov Models
Jeremie Houssineau, Ajay Jasra and Sumeetpal S. SinghSIAM Journal on Numerical Analysis 57 (6) 2812 (2019)
https://doi.org/10.1137/18M1198004
On the two-filter approximations of marginal smoothing distributions in general state-space models
Thi Ngoc Minh Nguyen, Sylvain Le Corff and Eric MoulinesAdvances in Applied Probability 50 (01) 154 (2018)
https://doi.org/10.1017/apr.2018.8
A sharp first order analysis of Feynman–Kac particle models, Part I: Propagation of chaos
Pierre Del Moral and Ajay JasraStochastic Processes and their Applications 128 (1) 332 (2018)
https://doi.org/10.1016/j.spa.2017.04.007
Efficient particle-based online smoothing in general hidden Markov models: The PaRIS algorithm
Jimmy Olsson and Johan WesterbornBernoulli 23 (3) (2017)
https://doi.org/10.3150/16-BEJ801
Consistent Estimation of the Filtering and Marginal Smoothing Distributions in Nonparametric Hidden Markov Models
Yohann De Castro, Elisabeth Gassiat and Sylvain Le CorffIEEE Transactions on Information Theory 63 (8) 4758 (2017)
https://doi.org/10.1109/TIT.2017.2696959
Convergence of sequential quasi-Monte Carlo smoothing algorithms
Mathieu Gerber and Nicolas ChopinBernoulli 23 (4B) (2017)
https://doi.org/10.3150/16-BEJ834
Calculating Principal Eigen-Functions of Non-Negative Integral Kernels: Particle Approximations and Applications
Nick Whiteley and Nikolas KantasMathematics of Operations Research 42 (4) 1007 (2017)
https://doi.org/10.1287/moor.2016.0834
Approximate Smoothing and Parameter Estimation in High-Dimensional State-Space Models
Axel Finke and Sumeetpal S. SinghIEEE Transactions on Signal Processing 65 (22) 5982 (2017)
https://doi.org/10.1109/TSP.2017.2733504
On particle Gibbs samplers
P. Del Moral, R. Kohn and F. PatrasAnnales de l'Institut Henri Poincaré, Probabilités et Statistiques 52 (4) (2016)
https://doi.org/10.1214/15-AIHP695
Particle Approximations of the Score and Observed Information Matrix for Parameter Estimation in State–Space Models With Linear Computational Cost
Christopher Nemeth, Paul Fearnhead and Lyudmila MihaylovaJournal of Computational and Graphical Statistics 25 (4) 1138 (2016)
https://doi.org/10.1080/10618600.2015.1093492
On Particle Methods for Parameter Estimation in State-Space Models
Nikolas Kantas, Arnaud Doucet, Sumeetpal S. Singh, Jan Maciejowski and Nicolas ChopinStatistical Science 30 (3) (2015)
https://doi.org/10.1214/14-STS511
Approximate Bayesian Computation for a Class of Time Series Models
Ajay JasraInternational Statistical Review 83 (3) 405 (2015)
https://doi.org/10.1111/insr.12089
On the Behaviour of the Backward Interpretation of Feynman-Kac Formulae Under Verifiable Conditions
Ajay JasraJournal of Applied Probability 52 (2) 339 (2015)
https://doi.org/10.1239/jap/1437658602
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
Pierre Del Moral, Arnaud Doucet and Sumeetpal S. SinghSIAM Journal on Control and Optimization 53 (3) 1278 (2015)
https://doi.org/10.1137/140993703
Error Propagation in Gossip-Based Distributed Particle Filters
Syamantak Datta Gupta, Mark Coates and Michael RabbatIEEE Transactions on Signal and Information Processing over Networks 1 (3) 148 (2015)
https://doi.org/10.1109/TSIPN.2015.2471846
On the Behaviour of the Backward Interpretation of Feynman-Kac Formulae Under Verifiable Conditions
Ajay JasraJournal of Applied Probability 52 (02) 339 (2015)
https://doi.org/10.1017/S002190020001250X
Approximate Bayesian Computation for Smoothing
James S. Martin, Ajay Jasra, Sumeetpal S. Singh, et al.Stochastic Analysis and Applications 32 (3) 397 (2014)
https://doi.org/10.1080/07362994.2013.879262
An Online Expectation–Maximization Algorithm for Changepoint Models
Sinan Yildirim, Sumeetpal S. Singh and Arnaud DoucetJournal of Computational and Graphical Statistics 22 (4) 906 (2013)
https://doi.org/10.1080/10618600.2012.674653
Convergence of a Particle-Based Approximation of the Block Online Expectation Maximization Algorithm
Sylvain Le Corff and Gersende FortACM Transactions on Modeling and Computer Simulation 23 (1) 1 (2013)
https://doi.org/10.1145/2414416.2414418
Non-asymptotic deviation inequalities for smoothed additive functionals in nonlinear state-space models
Cyrille Dubarry and Sylvain Le CorffBernoulli 19 (5B) (2013)
https://doi.org/10.3150/12-BEJ450
Likelihood computation for hidden Markov models via generalized two-filter smoothing
Adam Persing and Ajay JasraStatistics & Probability Letters 83 (5) 1433 (2013)
https://doi.org/10.1016/j.spl.2013.02.005
Stability properties of some particle filters
Nick WhiteleyThe Annals of Applied Probability 23 (6) (2013)
https://doi.org/10.1214/12-AAP909
Constructing Summary Statistics for Approximate Bayesian Computation: Semi-Automatic Approximate Bayesian Computation
Paul Fearnhead and Dennis PrangleJournal of the Royal Statistical Society Series B: Statistical Methodology 74 (3) 419 (2012)
https://doi.org/10.1111/j.1467-9868.2011.01010.x
Numerical Methods in Finance
René Carmona, Pierre Del Moral, Peng Hu and Nadia OudjaneSpringer Proceedings in Mathematics, Numerical Methods in Finance 12 3 (2012)
https://doi.org/10.1007/978-3-642-25746-9_1
On the Robustness of the Snell Envelope
Pierre Del Moral, Peng Hu, Nadia Oudjane and Bruno RémillardSIAM Journal on Financial Mathematics 2 (1) 587 (2011)
https://doi.org/10.1137/100798016
Sequential Monte Carlo Methods for Option Pricing
Ajay Jasra and Pierre Del MoralStochastic Analysis and Applications 29 (2) 292 (2011)
https://doi.org/10.1080/07362994.2011.548993
197 (2011)
https://doi.org/10.1109/SSP.2011.5967657
Sequential Monte Carlo smoothing for general state space hidden Markov models
Randal Douc, Aurélien Garivier, Eric Moulines and Jimmy OlssonThe Annals of Applied Probability 21 (6) (2011)
https://doi.org/10.1214/10-AAP735
On the Stability and the Approximation of Branching Distribution Flows, with Applications to Nonlinear Multiple Target Filtering
F. Caron, P. Del Moral, M. Pace and B.-N. VoStochastic Analysis and Applications 29 (6) 951 (2011)
https://doi.org/10.1080/07362994.2011.598797
An introduction to probabilistic methods with applications
Pierre Del Moral and Nicolas G. HadjiconstantinouESAIM: Mathematical Modelling and Numerical Analysis 44 (5) 805 (2010)
https://doi.org/10.1051/m2an/2010043
3182 (2007)
https://doi.org/10.1109/CDC.2007.4434095
1574 (2006)
https://doi.org/10.1109/CDC.2006.377796