Iterated filtering

Iterated filtering algorithms are a tool for maximum likelihood inference on partially observed dynamical systems. Stochastic perturbations to the… (More)
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Topic mentions per year

Topic mentions per year

2009-2016
01220092016

Papers overview

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2017
2017
Simulation-based inference for partially observed stochastic dynamic models is currently receiving much attention due to the fact… (More)
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2017
2017
Infectious disease surveillance data often provides only partial information about the progression of the disease in the… (More)
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2016
2016
An iterated filtering method is presented to improve the update stage of nonlinear filtering. First, we develop a generalized… (More)
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Review
2015
Review
2015
Country-wide data from the 2008-2009 cholera epidemic in Zimbabwe came from the authors of Reyburn et al.4 Country-level data was… (More)
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2014
2014
A variety of filtering methods enable the recursive estimation of system state variables and inference of model parameters. These… (More)
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2014
2014
This paper investigates the adaptive sensing for cooperative target tracking in threedimensional environments by multiple… (More)
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2012
2012
In this project we propose a mixed graphical model that allows us to model data sets with both continuous and discrete variables… (More)
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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… (More)
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2010
2010
In this note we apply the particle-based iterated filtering algorithm proposed by Ionides et al. (2009) to the problem of… (More)
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2009
2009
Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and… (More)
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