Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems

Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Volumn , Issue , 2007, Pages 306-311

  Shen, Y ^a   Archambeau, C ^b   Cornford, D ^a   Opper, M ^c   Shawe Taylor, J ^b   Barillec, R ^a  

^a ASTON UNIVERSITY   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIFFERENCE EQUATIONS; DIFFERENTIAL EQUATIONS; DIFFERENTIATION (CALCULUS); LEARNING SYSTEMS; MANAGEMENT SCIENCE; MARKOV PROCESSES; MATHEMATICAL MODELS; ROBOT LEARNING; SECURITY OF DATA; SIGNAL PROCESSING; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC MODELS; STOCHASTIC PROGRAMMING;

DOUBLE-WELL POTENTIALS; EXACT SOLUTIONS; GAUSSIAN PROCESSES; IEEE SIGNAL PROCESSING SOCIETY; MACHINE-LEARNING; MARKOV CHAIN MONTE CARLO METHODS; MONTE CARLO APPROACH; OBSERVED SYSTEMS; PATH SAMPLING; STOCHASTIC DIFFERENTIAL EQUATIONS; STOCHASTIC DYNAMIC SYSTEMS; VARIATIONAL INFERENCE;

MONTE CARLO METHODS;

EID: 48149106949     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2007.4414324     Document Type: Conference Paper

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