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Iterated Filtering
, 2011
"... Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of fi ..."
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Cited by 14 (3 self)
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Inference for partially observed Markov process models has been a longstanding methodological challenge with many scientific and engineering applications. Iterated filtering algorithms maximize the likelihood function for partially observed Markov process models by solving a recursive sequence of filtering problems. We present new theoretical results pertaining to the convergence of iterated filtering algorithms implemented via sequential Monte Carlo filters. This theory complements the growing body of empirical evidence that iterated filtering algorithms provide an effective inference strategy for scientific models of nonlinear dynamic systems. The first step in our theory involves studying a new recursive approach for maximizing the likelihood function of a latent variable model, when this likelihood is evaluated via importance sampling. This leads to the consideration of an iterated importance sampling algorithm which serves as a simple special case of iterated filtering, and may have applicability in its own right. 1
Asymptotic efficiency and finitesample properties of the generalized profiling estimation of . . .
, 2009
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Inference for SDE models via approximate Bayesian computation
 Journal of Computational and Graphical Statistics
"... Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing in many applied research areas and is already a standard tool to model e.g. financial, neuronal and population growth d ..."
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Cited by 4 (3 self)
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Models defined by stochastic differential equations (SDEs) allow for the representation of random variability in dynamical systems. The relevance of this class of models is growing in many applied research areas and is already a standard tool to model e.g. financial, neuronal and population growth dynamics. However inference for multidimensional SDE models is still very challenging, both computationally and theoretically. Approximate Bayesian computation (ABC) allow to perform Bayesian inference for models which are sufficiently complex that the likelihood function is either analytically unavailable or computationally prohibitive to evaluate. A computationally efficient ABCMCMC algorithm is proposed, halving the running time in our simulations. Focus is on the case where the SDE describes latent dynamics in statespace models; however the methodology is not limited to the statespace framework. Simulation studies for a pharmacokinetics/pharmacodynamics model and for stochastic chemical reactions are considered and a Matlab package implementing our ABCMCMC algorithm is provided.
A generic multivariate distribution for counting data, Arxiv preprint arXiv:1103.4866
"... Motivated by the need, in some Bayesian likelihood free inference problems, of imputing a multivariate counting distribution based on its vector of means and variancecovariance matrix, we define a generic multivariate discrete distribution. Based on blending the Binomial, Poisson and NegativeBinom ..."
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Cited by 1 (0 self)
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Motivated by the need, in some Bayesian likelihood free inference problems, of imputing a multivariate counting distribution based on its vector of means and variancecovariance matrix, we define a generic multivariate discrete distribution. Based on blending the Binomial, Poisson and NegativeBinomial distributions, and using a normal multivariate copula, the required distribution is defined. This distribution tends to the Multivariate Normal for large counts and has an approximate pmf version that is quite simple to evaluate.
RAPIDD: Research and Policy in Infectious Disease Dynamics
, 2008
"... Ed Ionides Time series analysis of infectious disease dynamics 1 Time series analysis of infectious disease dynamics: State of the art and future challenges Epidemic model hierarchies and model validation workshop ..."
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Ed Ionides Time series analysis of infectious disease dynamics 1 Time series analysis of infectious disease dynamics: State of the art and future challenges Epidemic model hierarchies and model validation workshop
Ed Ionides Infectious disease dynamics: a statistical perspective 1 Infectious disease dynamics: a statistical perspective CCMB/Bioinformatics Seminar
, 2009
"... Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 influenza “bird flu”) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA “the hospital superbug”) Ed Ionides Infe ..."
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Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 influenza “bird flu”) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA “the hospital superbug”) Ed Ionides Infectious disease dynamics: a statistical perspective 4 Disease dynamics: epidemiology or ecology, or both? • Environmental host/pathogen dynamics are close to predator/prey relationships which are a central topic of ecology. • Analysis of diseases as ecosystems complements more traditional epidemiology (risk factors etc). • Ecologists typically seek to avoid extinctions, whereas epidemiologists typically seek the reverse. Things are not always this simple... – Helicobacter pylori bacteria used to live in the stomach of most humans. Some strains cause stomach ulcers and cancer. It is almost extinct in the developed world due to widespread use of
Collaborators
, 2009
"... Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 “bird flu ” influenza, H1N1 “swine flu ” influenza) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA) Ed Ionides ..."
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Why do we seek to quantify and understand disease dynamics? • Prevention and control of emerging infectious diseases (SARS, HIV/AIDS, H5N1 “bird flu ” influenza, H1N1 “swine flu ” influenza) • Understanding the development and spread of drug resistant strains (malaria, tuberculosis, MRSA) Ed Ionides Infectious disease dynamics: a statistical perspective 4 Disease dynamics: epidemiology or ecology, or both? • Environmental host/pathogen dynamics are close to predator/prey relationships which are a central topic of ecology. • Analysis of diseases as ecosystems complements more traditional epidemiology (risk factors etc). • Ecologists typically seek to avoid extinctions, whereas epidemiologists typically seek the reverse. Ed Ionides Infectious disease dynamics: a statistical perspective 5 Infectious disease transmission: the statistical challenge • Time series data of sufficient quantity and quality to support investigations of disease dynamics are increasingly available.