On convergence and accuracy of state-space approximations of squared exponential covariance functions

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

Volumn , Issue , 2014, Pages

  Särkkä, Simo ^a   Piché, Robert ^b  

^a AALTO UNIVERSITY   (Finland)

^b TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

central limit theorem; Gaussian process regression; Kalman filter and smoother; Pad approximant; squared exponential; state space approximation

Indexed keywords

EXPONENTIAL FUNCTIONS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); KALMAN FILTERS; LEARNING SYSTEMS; SIGNAL PROCESSING; SPECTRAL DENSITY;

APPROXIMANTS; CENTRAL LIMIT THEOREM; GAUSSIAN PROCESS REGRESSION; KALMAN FILTER AND SMOOTHERS; SQUARED EXPONENTIAL;

APPROXIMATION ALGORITHMS;

EID: 84912569556     PISSN: 21610363     EISSN: 21610371     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2014.6958890     Document Type: Conference Paper

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