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This paper discusses particle filtering in general hidden Markov models (HMMs) and presents novel theoretical results on the long-term stability of bootstrap-type particle filters.
van Handel, RamonR. (2009). The stability of conditional Markov processes and Markov chains in random environments. Ann. Probab. 37 1876–1925
1925
Earlier work this paper cites.
Kingman, J. F. C.J. F. C. (1973). Subadditive ergodic theory. Ann. Probab. 1 883–909
1973
Earlier work this paper cites.
Pólya, G.G. andSzegő, G.G. (1976). Problems and Theorems in Analysis. Springer, New York
1976
Earlier work this paper cites.
Serfling, Robert J.R. J. (1980). Approximation Theorems of Mathematical Statistics. Wiley, New York
1980
Earlier work this paper cites.
Gordon, N.N., Salmond, D.D. andSmith, A. F.A. F. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc., F, Radar Signal Process. 140 107–113
1993
Earlier work this paper cites.
Shiryaev, A. N.A. N. (1996). Probability, 2nd ed. Springer, New York
1996
Earlier work this paper cites.
Del Moral, P.P. andGuionnet, A.A. (1999). Central limit theorem for nonlinear filtering and interacting particle systems. Ann. Appl. Probab. 9 275–297
1999
Earlier work this paper cites.
Pitt, Michael K.M. K. andShephard, NeilN. (1999). Filtering via simulation: Auxiliary particle filters. J. Amer. Statist. Assoc. 94 590–599
1999
Earlier work this paper cites.
Del Moral, P.P. andLedoux, M.M. (2000). Convergence of empirical processes for interacting particle systems with applications to nonlinear filtering. J. Theoret. Probab. 13 225–257
2000
Earlier work this paper cites.
Del Moral, P.P. andMiclo, L.L. (2000). Branching and interacting particle systems approximations of Feynman–Kac formulae with applications to nonlinear filtering. In Séminaire de Probabilités, XXXIV. Lecture Notes in Math. 1729 1–145. Springer, Berlin
2000
Earlier work this paper cites.
Del Moral, PierreP. andGuionnet, AliceA. (2001). On the stability of interacting processes with applications to filtering and genetic algorithms. Ann. Inst. Henri Poincaré Probab. Stat. 37 155–194
2001
Earlier work this paper cites.
Del Moral, PierreP., Jacod, JeanJ. andProtter, PhilipP. (2001). The Monte-Carlo method for filtering with discrete-time observations. Probab. Theory Related Fields 120 346–368
2001
Earlier work this paper cites.
Doucet ArnaudA., de Freitas NandoN. andGordon NeilN., eds. (2001). Sequential Monte Carlo Methods in Practice. Springer, New York
2001
Cited alongside, same era.
Chopin, NicolasN. (2002). A sequential particle filter method for static models. Biometrika 89 539–551
2002
Cited alongside, same era.
LeGland, FrançoisF. andOudjane, NadiaN. (2003). A robustification approach to stability and to uniform particle approximation of nonlinear filters: The example of pseudo-mixing signals. Stochastic Process. Appl. 106 279–316
2003
Cited alongside, same era.
Del Moral, PierreP. (2004). Feynman–Kac Formulae. Genealogical and Interacting Particle Systems with Applications. Springer, New York
2004
Cited alongside, same era.
Godsill, S. J.S. J., Doucet, A.A. andWest, M.M. (2004). Monte Carlo smoothing for nonlinear time series. J. Amer. Statist. Assoc. 50 438–449
2004
Douc, RandalR. andMoulines, EricE. (2008). Limit theorems for weighted samples with applications to sequential Monte Carlo methods. Ann. Statist. 36 2344–2376
2008
Later among the works it cites.
Heine, KariK. andCrisan, DanD. (2008). Uniform approximations of discrete-time filters. Adv. in Appl. Probab. 40 979–1001
2008
Later among the works it cites.
Johansen, Adam M.A. M. andDoucet, ArnaudA. (2008). A note on auxiliary particle filters. Statist. Probab. Lett. 78 1498–1504
2008
Later among the works it cites.
Kleptsyna, M. L.M. L. andVeretennikov, A. Yu.A. Yu. (2008). On discrete time ergodic filters with wrong initial data. Probab. Theory Related Fields 141 411–444
2008
Later among the works it cites.
Olsson, JimmyJ. andRydén, TobiasT. (2008). Asymptotic properties of particle filter-based maximum likelihood estimators for state space models. Stochastic Process. Appl. 118 649–680
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Cited alongside, same era.
Le Gland, FrançoisF. andOudjane, NadiaN. (2004). Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters. Ann. Appl. Probab. 14 144–187
2004
Cited alongside, same era.
Cappé, OlivierO., Moulines, EricE. andRydén, TobiasT. (2005). Inference in Hidden Markov Models. Springer, New York
2005
Cited alongside, same era.
Douc, R.R., Guillin, A.A. andNajim, J.J. (2005). Moderate deviations for particle filtering. Ann. Appl. Probab. 15 587–614
2005
Cited alongside, same era.
Oudjane, NadiaN. andRubenthaler, SylvainS. (2005). Stability and uniform particle approximation of nonlinear filters in case of nonergodic signals. Stoch. Anal. Appl. 23 421–448
2005
Cited alongside, same era.
Tadić, Vladislav B.V. B. andDoucet, ArnaudA. (2005). Exponential forgetting and geometric ergodicity for optimal filtering in general state–space models. Stochastic Process. Appl. 115 1408–1436
2005
Cited alongside, same era.
Crisan, D.D. andHeine, K.K. (2008). Stability of the discrete time filter in terms of the tails of noise distributions. J. Lond. Math. Soc. (2) 78 441–458
2008
Cited alongside, same era.
2008
Later among the works it cites.
Bain, AlanA. andCrisan, DanD. (2009). Fundamentals of Stochastic Filtering. Stochastic Modelling and Applied Probability 60. Springer, New York
2009
Later among the works it cites.
Douc, R.R., Fort, G.G., Moulines, E.E. andPriouret, P.P. (2009). Forgetting the initial distribution for hidden Markov models. Stochastic Process. Appl. 119 1235–1256
2009
Later among the works it cites.
Douc, RandalR., Moulines, ÉricÉ. andOlsson, JimmyJ. (2009). Optimality of the auxiliary particle filter. Probab. Math. Statist. 29 1–28
2009
Later among the works it cites.
Douc, RandalR., Garivier, AurélienA., Moulines, EricE. andOlsson, JimmyJ. (2011). Sequential Monte Carlo smoothing for general state space hidden Markov models. Ann. Appl. Probab. 21 2109–2145
2011
Later among the works it cites.
Douc, RandalR. andMoulines, EricE. (2012). Asymptotic properties of the maximum likelihood estimation in misspecified hidden Markov models. Ann. Statist. 40 2697–2732
2012
Closest in time.
Künsch, Hans R.H. R. (2005). Recursive Monte Carlo filters: Algorithms and theoretical analysis. Ann. Statist. 33 1983–2021
2021
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