Many scientific fields have experienced growth in the use of stochastic differential equations
(SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

This new edition updates Durbin & Koopman's important text on the state space approach to
time series analysis. The distinguishing feature of state space time series models is that …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

Since the first edition of Stochastic Modelling for Systems Biology, there have been many
interesting developments in the use of" likelihood-free" methods of Bayesian inference for …

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 …

This paper examines continuous‐time stochastic volatility models incorporating jumps in
returns and volatility. We develop a likelihood‐based estimation strategy and provide …

Stochastic di? erential equations model stochastic evolution as time evolves. These models
have a variety of applications in many disciplines and emerge naturally in the study of many …

This paper evaluates the role of various volatility specifications, such as multiple stochastic
volatility (SV) factors and jump components, in appropriate modeling of equity return …

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