Bayesian inference and learning in Gaussian process state-space models with Particle MCMC

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Frigola, Roger ^a   Lindsten, Fredrik ^b   Schön, Thomas B ^b,c   Rasmussen, Carl E ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^b LINKÖPING UNIVERSITY   (Sweden)

^c UPPSALA UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; DYNAMICAL SYSTEMS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INFERENCE ENGINES;

DYNAMICAL PHENOMENA; FULLY BAYESIAN APPROACHES; GAUSSIAN PROCESS PRIORS; PARTICLE MARKOV CHAIN MONTE CARLO; PREDICTIVE DISTRIBUTIONS; SPARSE GAUSSIAN PROCESS; STATE TRANSITION DYNAMICS; TRANSITION DYNAMICS;

MODELS;

EID: 84898996071     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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