skip to content
 

A methodological framework for Monte Carlo estimation of continuous-time processes

Presented by: 
O Papaspiliopoulos Universitat Pompeu Fabra
Date: 
Friday 20th June 2008 - 14:00 to 15:00
Venue: 
INI Seminar Room 1
Abstract: 

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.

The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons