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A Sequential Monte Carlo inference for longitudinal data with luespotted mud hopper data  

Choi, Il-Su (여수대학교 응용수학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.9, no.6, 2005 , pp. 1341-1345 More about this Journal
Abstract
Sequential Monte Carlo techniques are a set of powerful and versatile simulation-based methods to perform optimal state estimation in nonlinear non-Gaussian state-space models. We can use Monte Carlo particle filters adaptively, i.e. so that they simultaneously estimate the parameters and the signal. However, Sequential Monte Carlo techniques require the use of special panicle filtering techniques which suffer from several drawbacks. We consider here an alternative approach combining particle filtering and Sequential Hybrid Monte Carlo. We give some examples of applications in fisheries(luespotted mud hopper data).
Keywords
Sequential Hybrid Monte Carlo; state-space model;
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  • Reference
1 C. K. Carter and R. Kahn, Markov Chain Monte Carlo in Conditionally Gaussian State space models, Biometrika, vol 83-3, pp. 589-601, 1996   DOI   ScienceOn
2 J. Geweke and H. Tanizaki, Bayesian Estimation of State-Space Models Using the Metropolis-Hastings Algorithm within Gibbs Sampling, Unpublished manuscript, 1999
3 H. Tanizaki, On the Nonlinear and nonnormal filter using rejection sampling, IEEE transactions on automatic control, vol 44, pp 314-319, 1999   DOI   ScienceOn
4 B. P. Carlin, N. G. Polson and D. S. Stoffer, A Monte Carlo Approach to Nonnormal and nonlinear State Space Modeling, Journal of American Statistical Association, vol 87, pp. 493-500, 1992   DOI   ScienceOn
5 S. Duane, A. D. Kennedy, B. Pendleton, B and D. Roweth, Hybrid Monte Carlo, Physics Letters B, vol 195, pp. 216-222, 1984   DOI   ScienceOn
6 N. Shephard and M. K. Pitt, Likelihood analysis of non-Gaussian measurement time series. Biometrika vol 84, pp. 653-667, 1997   DOI   ScienceOn
7 H. Tanizaki, Nonlinear Filters: Estimation and Applications (Second, rivesed and enlarged edition), Springer-Verlag, 1996
8 C. K. Carter and R. Kohn, On Gibbs sampling for state space models, Biometrika, vol 81-3, pp. 541-553, 1994   DOI   ScienceOn