PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Variational Inference and Learning for Non-Linear State-Space Models with State-Dependent Observation Noise
Veli Peltola and Antti Honkela
In: 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), 29 Aug - 1 Sep 2010, Kittilä, Finland.

Abstract

In many real world dynamical systems, the inherent noise levels are not constant but depend on the state. Such aspects are often ignored in modelling because they make inference significantly more complicated. In this paper we propose a variational inference and learning algorithm for a non-linear state-space model with state-dependent observation noise. The observation noise level of each sample depends on additional latent variables with a linear dependence on the latent state. The method yields significant improvements in predictive performance over regular nonlinear state-space model as well as direct autoregressive prediction using Gaussian processes in a simulated Lorenz system with state-dependent noise and in stock price prediction.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7753
Deposited By:Antti Honkela
Deposited On:17 March 2011