Bayesian nonparametric inference of switching dynamic linear models

IEEE Transactions on Signal Processing

Volumn 59, Issue 4, 2011, Pages 1569-1585

  Fox, Emily ^a   Sudderth, Erik B ^b   Jordan, Michael I ^c,d   Willsky, Alan S ^e  

^a Duke University   (United States)

^b Brown Univerity   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA   (United States)

^e Department of Electrical Engineering and Computer Science   (United States)

Author keywords

Autoregressive processes; Bayesian methods; hidden Markov models; state space methods; time series analysis; unsupervised learning

Indexed keywords

AUTO REGRESSIVE PROCESS; AUTO-REGRESSIVE; AUTOMATIC RELEVANCE DETERMINATION; BAYESIAN; BAYESIAN METHODS; DIRICHLET PROCESS; DYNAMIC DEPENDENCY; DYNAMICAL PHENOMENA; HIERARCHICAL DIRICHLET PROCESS; HONEY BEE; MANEUVERING TARGET TRACKING; NONPARAMETRIC APPROACHES; NONPARAMETRIC INFERENCE; SAMPLING ALGORITHM; SPARSE SET; STATE SEQUENCES; STOCK INDICES; SWITCHING DYNAMICS; SWITCHING LINEAR DYNAMICAL SYSTEMS; SWITCHING VECTOR; SYNTHETIC DATA; VAR PROCESS;

APPROXIMATION ALGORITHMS; BAYESIAN NETWORKS; DYNAMICAL SYSTEMS; LINEAR CONTROL SYSTEMS; STATE SPACE METHODS; SWITCHING; TARGET TRACKING; TIME SERIES; TIME SERIES ANALYSIS; UNSUPERVISED LEARNING; VALUE ENGINEERING;

HIDDEN MARKOV MODELS;

EID: 79952690893     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2010.2102756     Document Type: Article

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