We consider analysis of complex stochastic models based upon partial information. MCMC
and reversible jump MCMC are often the methods of choice for such problems, but in some …

A stochastic discrete-time susceptible-exposed-infectious-recovered (SEIR) model for
infectious diseases is developed with the aim of estimating parameters from daily incidence …

We demonstrate how to perform direct simulation from the posterior distribution of a class of
multiple changepoint models where the number of changepoints is unknown. The class of …

Likelihood-based inference for epidemic models can be challenging, in part due to
difficulties in evaluating the likelihood. The problem is particularly acute in models of large …

In this paper we review sequential Monte Carlo (SMC) methods, or particle filters (PF), with
special emphasis on its potential applications in financial time series analysis and …

We consider inference for the reaction rates in discretely observed networks such as those
found in models for systems biology, population ecology, and epidemics. Most such …

We consider regression models where the underlying functional relationship between the
response and the explanatory variable is modeled as independent linear regressions on …

A powerful and flexible method for fitting dynamic models to missing and censored data is to
use the Bayesian paradigm via data-augmented Markov chain Monte Carlo (DA-MCMC) …

Recently-proposed particle MCMC methods provide a flexible way of performing Bayesian
inference for parameters governing stochastic kinetic models defined as Markov (jump) …

Concerns over bioterrorism and emerging diseases have led to the widespread use of
epidemic models for evaluating public health strategies. Partly because epidemic models …