Projected sequential Gaussian processes: A C++ tool for interpolation of large datasets with heterogeneous noise

Computers and Geosciences

Volumn 37, Issue 3, 2011, Pages 295-309

  Barillec, Remi ^a   Ingram, Ben ^b   Cornford, Dan ^a   Csató, Lehel ^c  

^a ASTON UNIVERSITY   (United Kingdom)

^b UNIVERSIDAD DE TALCA   (Chile)

^c BABE BOLYAI UNIVERSITY   (Romania)

Author keywords

Heterogeneous data; Low rank approximations; Sensor fusion

Indexed keywords

BAYESIAN; BAYESIAN APPROACHES; BAYESIAN FRAMEWORKS; C++ LIBRARIES; COVARIANCE FUNCTION; COVARIANCE PARAMETERS; DATA SETS; ERROR CHARACTERISTICS; GAUSSIAN PROCESSES; GEOSTATISTICAL APPROACH; HETEROGENEOUS DATA; HETEROGENEOUS DATASETS; HIGH-DIMENSIONAL INTEGRALS; LARGE DATASETS; LOW-RANK APPROXIMATIONS; MACHINE-LEARNING; MINIMAL INFORMATION; MODEL-BASED; MONTE CARLO; NON-GAUSSIAN; OBSERVATION ERRORS; OBSERVATION OPERATOR; REAL DATA SETS; REDUCED RANK; SENSOR FUSION; SENSOR MODEL; SEQUENTIAL ESTIMATION; SEQUENTIAL METHODS; SPATIAL STATISTICS;

BAYESIAN NETWORKS; COVARIANCE MATRIX; GAUSSIAN NOISE (ELECTRONIC); MONTE CARLO METHODS; SENSORS; WEB SERVICES;

SENSOR DATA FUSION;

BAYESIAN ANALYSIS; COVARIANCE ANALYSIS; DATA SET; ERROR ANALYSIS; GEOSTATISTICS; MATRIX; MONTE CARLO ANALYSIS; SENSOR;

EID: 79952196502     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2010.05.008     Document Type: Article

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