From Data to Knowledge - 303 - Simon Wilson
Channel: ckleinastro
Date: 09/05/2012

Slides: http://lyra.berkeley.edu/CDIConf/pdfs/Wilson_statespacemodelestimation.pdf

Simon Wilson: "Sequential Bayesian parameter estimation in state space models". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012).

Abstract
Simon Wilson (Trinity College Dublin, CS & Stats)
The goal of this work is to propose and evaluate a set of real-time methods for Bayesian estimation of the parameters of a state space model. While most state space inference procedures concentrate on the prediction and smoothing tasks, model parameter estimation can also play an important role in some applications. This work starts with the observation that any solution that provides prediction and smoothing densities can be used to derive the posterior distribution of the parameters of a state space model. We evaluate approaches based on Laplace approximations, various generalisations of the Kalman filter, along with corrections that improve these approximations. They are evaluated with non-linear and non-Gaussian state space models. It is shown that, when the parameter space is of modest dimension, a real-time Bayesian parameter estimation method can be successfully implemented.