Name
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TSP.2010.2102756 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
unsupervised learning,nonparametric statistics,synthetic data,vector autoregression,complex dynamics,signal processing,hierarchical dirichlet process,autoregressive process,bayesian methods,hidden markov model,bayesian method,sampling methods,switches,linear dynamical system,time series analysis,linear systems,hidden markov models | Hierarchical Dirichlet process,Linear dynamical system,Autoregressive model,Mathematical optimization,Dirichlet process,Linear system,Linear model,Hidden Markov model,Dynamical system,Mathematics | Journal |
Volume | Issue | ISSN |
59 | 4 | 1053-587X |
Citations | PageRank | References |
49 | 2.20 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emily B. Fox | 1 | 542 | 39.91 |
| Erik B. Sudderth | 2 | 1420 | 119.04 |
| Michael I. Jordan | 3 | 31220 | 3640.80 |
| Alan S. Willsky | 4 | 7466 | 847.01 |