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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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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
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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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2011
2011
Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and… Expand
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2010
2010
1. Partially-observed Markov processes 1 2. A first example: a discrete-time bivariate autoregressive process. 3 3. Defining a… 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
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2003
2003
The paper analyses the effects and problems of iterated processing in the SAR image filter, and provides a new iterated filtering… Expand
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