Fetching the paper…
Reading the bibliography…
We present an offline, iterated particle filter to facilitate statistical inference in general state space hidden Markov models.
Nadaraya, E. A. (1964), ‘On estimating regression’, Theory of Probability & Its Applications
1964
Earlier work this paper cites.
Watson, G. S. (1964), ‘Smooth regression analysis’, Sankhyā: The Indian Journal of Statistics, Series A
1964
Earlier work this paper cites.
Walker, A. J. (1974), ‘New fast method for generating discrete random numbers with arbitrary frequency distributions’, Electronics Letters
1974
Earlier work this paper cites.
Walker, A. J. (1977), ‘An efficient method for generating discrete random variables with general distributions’, ACM Transactions on Mathematical Software
1977
Earlier work this paper cites.
Lerman, S. & Manski, C. (1981), On the use of simulated frequencies to approximate choice probabilities, in
1981
Earlier work this paper cites.
Diggle, P. J. & Gratton, R. J. (1984), ‘Monte Carlo methods of inference for implicit statistical models’, Journal of the Royal Statistical Society. Series B (Methodological)
1984
Earlier work this paper cites.
Gordon, N. J., Salmond, D. J. & Smith, A. F. (1993), ‘Novel approach to nonlinear/non-Gaussian Bayesian state estimation’, IEE Proceedings-Radar, Sonar and Navigation
1993
Earlier work this paper cites.
Harvey, A., Ruiz, E. & Shephard, N. (1994), ‘Multivariate stochastic variance models’, The Review of Economic Studies
1994
Earlier work this paper cites.
Kong, A., Liu, J. S. & Wong, W. H. (1994), ‘Sequential imputations and Bayesian missing data problems’, Journal of the American Statistical Association
1994
Earlier work this paper cites.
Liu, J. S. & Chen, R. (1995), ‘Blind deconvolution via sequential imputations’, Journal of the American Statistical Association
1995
Earlier work this paper cites.
Kim, S., Shephard, N. & Chib, S. (1998), ‘Stochastic volatility: likelihood inference and comparison with arch models’, The Review of Economic Studies
1998
Earlier work this paper cites.
Kitagawa, G. (1998), ‘A self-organizing state-space model’, Journal of the American Statistical Association
1998
Earlier work this paper cites.
Clapp, T. C. & Godsill, S. J. (1999), ‘Fixed-lag smoothing using sequential importance sampling’, Bayesian Statistics 6: Proceedings of the Sixth Valencia International Meeting
1999
Earlier work this paper cites.
Del Moral, P. & Guionnet, A. (1999), ‘Central limit theorem for nonlinear filtering and interacting particle systems’, The Annals of Applied Probability
1999
Earlier work this paper cites.
Pitt, M. K. & Shephard, N. (1999), ‘Filtering via simulation: Auxiliary particle filters’, Journal of the American Statistical Association
1999
Cited alongside, same era.
Vidoni, P. (1999), ‘Exponential family state space models based on a conjugate latent process’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
1999
Cited alongside, same era.
Doucet, A., Godsill, S. & Andrieu, C. (2000), ‘On sequential Monte Carlo sampling methods for Bayesian filtering’, Statistics and Computing
2000
Cited alongside, same era.
Hürzeler, M. & Künsch, H. R. (2001), Approximating and maximising the likelihood for a general state-space model, in
2001
Cited alongside, same era.
Liu, J. & West, M. (2001), Combined parameter and state estimation in simulation-based filtering, in
2001
Cited alongside, same era.
Chib, S., Omori, Y. & Asai, M. (2009), Multivariate stochastic volatility, in
2009
Later among the works it cites.
Andrieu, C., Doucet, A. & Holenstein, R. (2010), ‘Particle Markov chain Monte Carlo methods’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2010
Later among the works it cites.
Doucet, A. & Johansen, A. M. (2011), A tutorial on particle filtering and smoothing: Fiteen years later, in
2011
Later among the works it cites.
Lin, M., Chen, R., Liu, J. S. et al. (2013), ‘Lookahead strategies for sequential Monte Carlo’, Statistical Science
2013
Later among the works it cites.
Bérard, J., Del Moral, P. & Doucet, A. (2014), ‘A lognormal central limit theorem for particle approximations of normalizing constants’, Electronic Journal of Probability
2014
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Beaumont, M. A. (2003), ‘Estimation of population growth or decline in genetically monitored populations’, Genetics
2003
Cited alongside, same era.
Chopin, N. (2004), ‘Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference’, The Annals of Statistics
2004
Cited alongside, same era.
Del Moral, P. (2004), Feynman-Kac Formulae
2004
Cited alongside, same era.
Douc, R., Cappé, O. & Moulines, E. (2005), Comparison of resampling schemes for particle filtering, in
2005
Cited alongside, same era.
Doucet, A., Briers, M. & Sénécal, S. (2006), ‘Efficient block sampling strategies for sequential Monte Carlo methods’, Journal of Computational and Graphical Statistics
2006
Cited alongside, same era.
Fernández-Villaverde, J. & Rubio-Ramírez, J. F. (2007), ‘Estimating macroeconomic models: A likelihood approach’, The Review of Economic Studies
2007
Cited alongside, same era.
Douc, R. & Moulines, E. (2008), ‘Limit theorems for weighted samples with applications to sequential Monte Carlo methods’, The Annals of Statistics
2008
Cited alongside, same era.
Lee, A. & Łatuszyński, K. (2014), ‘Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation.’, Biometrika
2014
Later among the works it cites.
Whiteley, N. & Lee, A. (2014), ‘Twisted particle filters’, The Annals of Statistics
2014
Later among the works it cites.
Andrieu, C. & Vihola, M. (2015), ‘Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms’, The Annals of Applied Probability
2015
Closest in time.
Doucet, A., Pitt, M., Deligiannidis, G. & Kohn, R. (2015), ‘Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator’, Biometrika
2015
Closest in time.
2015
Closest in time.
2015
Closest in time.
Sherlock, C., Thiery, A. H., Roberts, G. O. & Rosenthal, J. S. (2015), ‘On the efficiency of pseudo-marginal random walk Metropolis algorithms’, The Annals of Statistics
2015
Closest in time.
Künsch, H. (2005), ‘Recursive Monte Carlo filters: algorithms and theoretical analysis’, The Annals of Statistics
2021
Closest in time.
Chen, R., Wang, X. & Liu, J. S. (2000), ‘Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering’, Information Theory, IEEE Transactions on
2094
Closest in time.