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Four essays on sequential Monte Carlo and quasi-Monte Carlo methods

, 2014
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Sequential Monte Carlo Methods in Practice

Technometrics, 2003
(2003). Sequential Monte Carlo Methods in Practice. Technometrics: Vol. 45, No. 1, pp. 106-106.
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Sequential Monte Carlo Methods

2015
This chapter analyzes Sequential Monte Carlo (SMC) algorithms and how they were initially developed to solve filtering problems that arise in nonlinear state–space models. The first paper that applied SMC techniques to posterior inference in DSGE models is Creal (2007).
Edward P. Herbst, Frank Schorfheide
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Sequential Monte Carlo methods

2009
In Chapter 2 we introduced the filtering recursion for general state space models (Proposition 2.1). The recursive nature of the algorithm that, from the filtering distribution at time t?1 and the observation yt computes the filtering distribution at time t, makes it ideally suited for a large class of applications in which inference must be made ...
Giovanni Petris, Sonia Petrone, Patrizia Campagnoli   +2 more
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Generation path-switching in sequential Monte-Carlo methods

2012 IEEE Congress on Evolutionary Computation, 2012
Traditional sequential Monte-Carlo methods suffer from weight degeneracy which is where the number of distinct particles collapse. This is a particularly debilitating problem in many practical applications. A new method, the adaptive path particle filter, based on the generation gap concept from evolutionary computation, is proposed for recursive ...
Ayub Hanif, Robert E. Smith 0001
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A Deterministic Sequential Monte Carlo Method for Haplotype Inference

IEEE Journal of Selected Topics in Signal Processing, 2008
Sets of single nucleotide polymorphisms (SNPs), or haplotypes, are widely used in the analysis of relationship between genetics and diseases. Due to the cost of obtaining exact haplotype pairs, genotypes which contain the unphased information corresponding to the haplotype pairs in the test subjects are used. Various haplotype inference algorithms have
Kuo-ching Liang, Xiaodong Wang 0001
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Superimposed Event Detection by Sequential Monte Carlo Methods

2007 IEEE 15th Signal Processing and Communications Applications, 2007
In this paper, we consider the detection of rare events by applying particle filtering. We model the rare event as an AR signal superposed on a background signal. The activation and deactivation times of the AR-signal are unknown. We solve the online detection problem of this superpositional rare event by extending the state space dimension by one. The
Urfalıoğlu, O., Kuruoğlu, E. E., Çetin, A. Enis   +2 more
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