Titlebook: Sequential Monte Carlo Methods in Practice; Arnaud Doucet,Nando Freitas,Neil Gordon Book 2001 Springer Science+Business Media New York 200

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Combined Parameter and State Estimation in Simulation-Based Filteringsing. We simply do not have access to efficient and effective methods of treating this problem, especially in models with realistically large numbers of fixed model parameters. It is a very challenging problem.

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An Introduction to Sequential Monte Carlo Methodsng radar measurements, estimating a digital communications signal using noisy measurements, or estimating the volatility of financial instruments using stock market data. Computational simplicity in the form of not having to store all the data might also be an additional motivating factor for sequential methods.

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Improvement Strategies for Monte Carlo Particle Filtersis volume) and (MacEachern, Clyde and Liu 1999). Finally, we present a generalisation of our own in which MCMC resampling ideas are used to traverse a sequence of ‘bridging’ densities which lie between the prediction density and the filtering density. In this way it is hoped to reduce the variabilit

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Posterior Cramér-Rao Bounds for Sequential Estimationptimal algorithm However, even if it is intractable to implement and run the optimal solution a lower bound on the performance of this solution can be obtained. The estimation error obtained with an optimal algorithm depends only on the fundamental properties of the model, e.g. signal-to-noise level

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Book 2001e series analysis.This will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris- XI Orsay in 1997.From 1998 to 2000, he conducted research at the Signal Processing Group of C

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