Master Class: Lecture 1: Particle Filtering and Smoothing for State-Space Models

SpeakerArnaud Doucet
AffiliationUniversity of Oxford
DateWednesday, 03 Oct 2012
Time13:00 - 14:00
LocationRoberts G06 Sir Ambrose Fleming LT
Event seriesMaster Class: Arnaud Doucet (1-5 Oct 2012)
Description

State-space models are a popular class of time series models which are ubiquitous in econometrics, ecology, robotics, signal processing, statistics etc.

Beyond finite state-space and linear Gaussian models, approximate inference in state-space models relies either on analytical or numerical approximations of the posterior distributions of interest.
Particle methods are a class of sequential Monte Carlo methods which are flexible, easily parallelizable and provide consistent estimates.

In this talk, I will review standard and advanced particle filtering and smoothing techniques. I will also discuss theoretical results which shed light on the performance of these approaches.

Slides: ucl_1.pdf

iCalendar csml_id_106.ics