Global InformationParticle filter information
Particle filters, or sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference.[1] The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of a Markov process, given the noisy and partial observations. The term "particle filters" was first coined in 1996 by Pierre Del Moral about mean-field interacting particle methods used in fluid mechanics since the beginning of the 1960s.[2] The term "Sequential Monte Carlo" was coined by Jun S. Liu and Rong Chen in 1998.[3]
Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of a stochastic process given the noisy and/or partial observations. The state-space model can be nonlinear and the initial state and noise distributions can take any form required. Particle filter techniques provide a well-established methodology[2][4][5] for generating samples from the required distribution without requiring assumptions about the state-space model or the state distributions. However, these methods do not perform well when applied to very high-dimensional systems.
Particle filters update their prediction in an approximate (statistical) manner. The samples from the distribution are represented by a set of particles; each particle has a likelihood weight assigned to it that represents the probability of that particle being sampled from the probability density function. Weight disparity leading to weight collapse is a common issue encountered in these filtering algorithms. However, it can be mitigated by including a resampling step before the weights become uneven. Several adaptive resampling criteria can be used including the variance of the weights and the relative entropy concerning the uniform distribution.[6] In the resampling step, the particles with negligible weights are replaced by new particles in the proximity of the particles with higher weights.
From the statistical and probabilistic point of view, particle filters may be interpreted as mean-field particle interpretations of Feynman-Kac probability measures.[7][8][9][10][11] These particle integration techniques were developed in molecular chemistry and computational physics by Theodore E. Harris and Herman Kahn in 1951, Marshall N. Rosenbluth and Arianna W. Rosenbluth in 1955,[12] and more recently by Jack H. Hetherington in 1984.[13] In computational physics, these Feynman-Kac type path particle integration methods are also used in Quantum Monte Carlo, and more specifically Diffusion Monte Carlo methods.[14][15][16] Feynman-Kac interacting particle methods are also strongly related to mutation-selection genetic algorithms currently used in evolutionary computation to solve complex optimization problems.
The particle filter methodology is used to solve Hidden Markov Model (HMM) and nonlinear filtering problems. With the notable exception of linear-Gaussian signal-observation models (Kalman filter) or wider classes of models (Benes filter[17]), Mireille Chaleyat-Maurel and Dominique Michel proved in 1984 that the sequence of posterior distributions of the random states of a signal, given the observations (a.k.a. optimal filter), has no finite recursion.[18] Various other numerical methods based on fixed grid approximations, Markov Chain Monte Carlo techniques, conventional linearization, extended Kalman filters, or determining the best linear system (in the expected cost-error sense) are unable to cope with large-scale systems, unstable processes, or insufficiently smooth nonlinearities.
Particle filters and Feynman-Kac particle methodologies find application in signal and image processing, Bayesian inference, machine learning, risk analysis and rare event sampling, engineering and robotics, artificial intelligence, bioinformatics,[19] phylogenetics, computational science, economics and mathematical finance, molecular chemistry, computational physics, pharmacokinetics, quantitative risk and insurance[20][21] and other fields.
- ^ Wills, Adrian G.; Schön, Thomas B. (3 May 2023). "Sequential Monte Carlo: A Unified Review". Annual Review of Control, Robotics, and Autonomous Systems. 6 (1): 159–182. doi:10.1146/annurev-control-042920-015119. ISSN 2573-5144. S2CID 255638127.
- ^ a b Del Moral, Pierre (1996). "Non Linear Filtering: Interacting Particle Solution" (PDF). Markov Processes and Related Fields. 2 (4): 555–580.
- ^ Liu, Jun S.; Chen, Rong (1998-09-01). "Sequential Monte Carlo Methods for Dynamic Systems". Journal of the American Statistical Association. 93 (443): 1032–1044. doi:10.1080/01621459.1998.10473765. ISSN 0162-1459.
- ^ Del Moral, Pierre (1998). "Measure Valued Processes and Interacting Particle Systems. Application to Non Linear Filtering Problems". Annals of Applied Probability. 8 (2) (Publications du Laboratoire de Statistique et Probabilités, 96-15 (1996) ed.): 438–495. doi:10.1214/aoap/1028903535.
- ^ Del Moral, Pierre (2004). Feynman-Kac formulae. Genealogical and interacting particle approximations. Springer. Series: Probability and Applications. p. 556. ISBN 978-0-387-20268-6.
- ^ Del Moral, Pierre; Doucet, Arnaud; Jasra, Ajay (2012). "On Adaptive Resampling Procedures for Sequential Monte Carlo Methods" (PDF). Bernoulli. 18 (1): 252–278. doi:10.3150/10-bej335. S2CID 4506682.
- ^ Del Moral, Pierre (2004). Feynman-Kac formulae. Genealogical and interacting particle approximations. Probability and its Applications. Springer. p. 575. ISBN 9780387202686.
Series: Probability and Applications
- ^ Del Moral, Pierre; Miclo, Laurent (2000). "Branching and Interacting Particle Systems Approximations of Feynman-Kac Formulae with Applications to Non-Linear Filtering". In Jacques Azéma; Michel Ledoux; Michel Émery; Marc Yor (eds.). Séminaire de Probabilités XXXIV (PDF). Lecture Notes in Mathematics. Vol. 1729. pp. 1–145. doi:10.1007/bfb0103798. ISBN 978-3-540-67314-9.
- ^ Del Moral, Pierre; Miclo, Laurent (2000). "A Moran particle system approximation of Feynman-Kac formulae". Stochastic Processes and Their Applications. 86 (2): 193–216. doi:10.1016/S0304-4149(99)00094-0. S2CID 122757112.
- ^ Cite error: The named reference
dp13was invoked but never defined (see the help page). - ^ Moral, Piere Del; Doucet, Arnaud (2014). "Particle methods: An introduction with applications". ESAIM: Proc. 44: 1–46. doi:10.1051/proc/201444001.
- ^ Rosenbluth, Marshall, N.; Rosenbluth, Arianna, W. (1955). "Monte-Carlo calculations of the average extension of macromolecular chains". J. Chem. Phys. 23 (2): 356–359. Bibcode:1955JChPh..23..356R. doi:10.1063/1.1741967. S2CID 89611599.
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ Cite error: The named reference
h84was invoked but never defined (see the help page). - ^ Del Moral, Pierre (2003). "Particle approximations of Lyapunov exponents connected to Schrödinger operators and Feynman-Kac semigroups". ESAIM Probability & Statistics. 7: 171–208. doi:10.1051/ps:2003001.
- ^ Assaraf, Roland; Caffarel, Michel; Khelif, Anatole (2000). "Diffusion Monte Carlo Methods with a fixed number of walkers" (PDF). Phys. Rev. E. 61 (4): 4566–4575. Bibcode:2000PhRvE..61.4566A. doi:10.1103/physreve.61.4566. PMID 11088257. Archived from the original (PDF) on 2014-11-07.
- ^ Caffarel, Michel; Ceperley, David; Kalos, Malvin (1993). "Comment on Feynman-Kac Path-Integral Calculation of the Ground-State Energies of Atoms". Phys. Rev. Lett. 71 (13): 2159. Bibcode:1993PhRvL..71.2159C. doi:10.1103/physrevlett.71.2159. PMID 10054598.
- ^ Ocone, D. L. (January 1, 1999). "Asymptotic stability of beneš filters". Stochastic Analysis and Applications. 17 (6): 1053–1074. doi:10.1080/07362999908809648. ISSN 0736-2994.
- ^ Maurel, Mireille Chaleyat; Michel, Dominique (January 1, 1984). "Des resultats de non existence de filtre de dimension finie". Stochastics. 13 (1–2): 83–102. doi:10.1080/17442508408833312. ISSN 0090-9491.
- ^ Hajiramezanali, Ehsan; Imani, Mahdi; Braga-Neto, Ulisses; Qian, Xiaoning; Dougherty, Edward R. (2019). "Scalable optimal Bayesian classification of single-cell trajectories under regulatory model uncertainty". BMC Genomics. 20 (Suppl 6): 435. arXiv:1902.03188. Bibcode:2019arXiv190203188H. doi:10.1186/s12864-019-5720-3. PMC 6561847. PMID 31189480.
- ^ Cruz, Marcelo G.; Peters, Gareth W.; Shevchenko, Pavel V. (2015-02-27). Fundamental Aspects of Operational Risk and Insurance Analytics: A Handbook of Operational Risk (1 ed.). Wiley. doi:10.1002/9781118573013. ISBN 978-1-118-11839-9.
- ^ Peters, Gareth W.; Shevchenko, Pavel V. (2015-02-20). Advances in Heavy Tailed Risk Modeling: A Handbook of Operational Risk (1 ed.). Wiley. doi:10.1002/9781118909560. ISBN 978-1-118-90953-9.