A methodological framework for Monte Carlo estimation of continuous-time processes
Seminar Room 1, Newton Institute
In this talk I will review a mathodological framework for the estimation of partially observed continuous-time processes using Monte Carlo methods. I will presente different types of data structures and frequency regimes and will focus on unbiased (with respect to discretization errors) Monte Carlo methods for parameter estimation and particle filtering of continuous-time processes. An important component of the methodology is the Poisson estimator and I will discuss some of its properties. I will also present some results on the parameter estimation using variations of the smooth particle filter which exploit the graphical model structure inherent in partially observed continuous-time Markov processes.
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