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Parameter Estimation in General State-Space Models using Particle Methods

Doucet, Arnaud; Tadic, Vladislav B. - In: Annals of the Institute of Statistical Mathematics : AISM 55 (2003) 2, pp. 409-422

Persistent link: https://www.econbiz.de/10006553570

Special issue on Nonlinear non-Gaussian models and related filtering methods - Optimal estimation and Cramér-Rao bounds for partial non-Gaussian state space models

Bergman, Niclas; Doucet, Arnaud; Gordon, Neil - In: Annals of the Institute of Statistical Mathematics : AISM 53 (2001) 1, pp. 97-112

Persistent link: https://www.econbiz.de/10006564326

Parameter estimation in general state-space models using particle methods

Doucet, Arnaud; Tadić, Vladislav - In: Annals of the Institute of Statistical Mathematics 55 (2003) 2, pp. 409-422

Persistent link: https://www.econbiz.de/10005616283

Smoothing algorithms for state–space models

Briers, Mark; Doucet, Arnaud; Maskell, Simon - In: Annals of the Institute of Statistical Mathematics 62 (2010) 1, pp. 61-89

Persistent link: https://www.econbiz.de/10008497341

Interacting sequential Monte Carlo samplers for trans-dimensional simulation

Jasra, Ajay; Doucet, Arnaud; Stephens, David A.; … - In: Computational Statistics & Data Analysis 52 (2008) 4, pp. 1765-1791

Persistent link: https://www.econbiz.de/10005130661

Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition

Jasra, Ajay; Doucet, Arnaud - In: Statistics & Probability Letters 78 (2008) 17, pp. 3062-3069

Sequential Monte Carlo (SMC) samplers [Del Moral, P., Doucet, A., Jasra, A., 2006. Sequential Monte Carlo samplers. J. Roy. Statist. Soc. B 68, 411-436] are designed to simulate from a sequence of probability measures on a common measurable space . One way to measure the accuracy of the...

Persistent link: https://www.econbiz.de/10005138379

Sequential Monte Carlo samplers

Moral, Pierre Del; Doucet, Arnaud; Jasra, Ajay - In: Journal of the Royal Statistical Society Series B 68 (2006) 3, pp. 411-436

We propose a methodology to sample sequentially from a sequence of probability distributions that are defined on a common space, each distribution being known up to a normalizing constant. These probability distributions are approximated by a cloud of weighted random samples which are propagated...

Persistent link: https://www.econbiz.de/10005140248

Discussion on the paper by Brooks, Giudici and Roberts

Robert, Christian P.; Meng, Xiao-Li; Møller, Jesper; … - In: Journal of the Royal Statistical Society Series B 65 (2003) 1, pp. 39-55

Persistent link: https://www.econbiz.de/10005140261

A note on auxiliary particle filters

Johansen, Adam M.; Doucet, Arnaud - In: Statistics & Probability Letters 78 (2008) 12, pp. 1498-1504

The auxiliary particle filter (APF) introduced by Pitt and Shephard [Pitt, M.K., Shephard, N., 1999. Filtering via simulation: Auxiliary particle filters. J. Am. Statist. Ass. 94, 590-599] is a very popular alternative to Sequential Importance Sampling and Resampling (SISR) algorithms to perform...

Persistent link: https://www.econbiz.de/10005223631

A policy gradient method for semi-Markov decision processes with application to call admission control

Singh, Sumeetpal S.; Tadic, Vladislav B.; Doucet, Arnaud - In: European Journal of Operational Research 178 (2007) 3, pp. 808-818

Persistent link: https://www.econbiz.de/10005253814