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Iterated filtering

Iterated filtering algorithms are a tool for maximum likelihood inference on partially observed dynamical systems. Stochastic perturbations to the… Expand
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Papers overview

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2019
2019
Dynamic epidemic models have proven valuable for public health decision makers as they provide useful insights into the… Expand
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2018
2018
In simulation-based inferences for partially observed Markov process models (POMP), the by-product of the Monte Carlo filtering… Expand
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2018
2018
Mathematical models have proven valuable for public health decision makers as they can provide insights into the understanding… Expand
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2016
2016
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Highly Cited
2015
Highly Cited
2015
Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools… Expand
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2015
2015
A recent special issue of the Journal of Mathematics and Music on mathematical theories of voice leading focused on the… Expand
2014
2014
Most of this work was undertaken at the University of Bath, where M.F. was a Ph.D. student, and it was supported in part by EPSRC… Expand
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2014
2014
This paper investigates the adaptive sensing for cooperative target tracking in three-dimensional environments by multiple… Expand
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2007
2007
The observational and kinematic models of GPS/INS loosely integrated navigation system are introduced.In order to control the… Expand
2003
2003
The paper analyses the effects and problems of iterated processing in the SAR image filter, and provides a new iterated filtering… Expand