Work

Bayesian Nonparametric Inference of Switching Dynamic Linear Models

Year: 2011

Type: article

Abstract: Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index and a maneuvering target tracking application.

Source: IEEE Transactions on Signal Processing

DOI: 10.1109/tsp.2010.2102756

Authors: Emily Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky

Institutions: Duke University, Brown University, University of California, Berkeley, Massachusetts Institute of Technology

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2109957730

FWCI: 11.19

Cites: 53

Cited by: 231

Related to: 9