Edward Ionides
: Slides for some presentations
An iterated block particle filter for inference on coupled dynamic systems
An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters
Inference for metapopulation dynamics
Bagging and blocking: Inference via particle filters for interacting dynamic systems dynamics
URPS 21: Undergraduate Research Program in Statistics for Winter 2021
Likelihood-based inference for partially observed processes, with applications to genetic sequences, panel data and spatiotemporal data
Monte Carlo adjusted profile likelihood, with applications to spatiotemporal and phylodynamic inference.
Remarks for Statistics Department graduation 2018
Likelihood-based inference for dynamic systems, with phylodynamic applications
Making nonlinear non-Gaussian state space models accessible to Masters level statisticians: A 5-minute summary of three courses
Likelihood-Based Inference for Partially Observed Spatiotemporal Dynamics: Measles as a Case Study
A short course on likelihood-based inference for dynamic systems
Introduction to likelihood-based inference for dynamic systems
Why do time series analysis? When is fitting a mechanistic model useful?
A new iterated filtering algorithm
A brief introduction to the pomp package for R
Using genetic sequences to infer population dynamics: Phylodynamic analysis of HIV transmission in SE Michigan
Sequential Monte Carlo methods for inferring transmission dynamics from pathogen genetic sequences
Inference for partially observed stochastic dynamic systems
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Epidemiological inference using stochastic dynamic models
Cell motility models and inference for dynamic systems
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Inferring biological dynamics:
[1]
,
[2]
,
[3]
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Inferring the dynamic mechanisms that drive ecological systems
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Feature matching versus likelihood: Nicholson's blowflies
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Time series analysis via mechanistic models
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Rapid loss of immunity is necessary to explain historical cholera epidemics
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The reversal of the relation between economic growth and health progress: Sweden in the 19th and 20th centuries
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Overdispersion for models of discrete populations
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