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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… 
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Papers overview

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2019
2019
  • Theresa Stocks
  • Handbook of Infectious Disease Data Analysis
  • 2019
  • Corpus ID: 88515911
Dynamic epidemic models have proven valuable for public health decision makers as they provide useful insights into the… 
2018
2018
In simulation-based inferences for partially observed Markov process models (POMP), the by-product of the Monte Carlo filtering… 
2018
2018
Mathematical models have proven valuable for public health decision makers as they can provide insights into the understanding… 
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… 
2015
2015
A recent special issue of the Journal of Mathematics and Music on mathematical theories of voice leading focused on the… 
Highly Cited
2014
Highly Cited
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… 
2011
2011
Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and… 
2007
2007
The observational and kinematic models of GPS/INS loosely integrated navigation system are introduced.In order to control the… 
2003
2003
The paper analyses the effects and problems of iterated processing in the SAR image filter, and provides a new iterated filtering…