scholarly journals Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models

2018 ◽  
Vol 84 (4) ◽  
pp. 203-214 ◽  
Author(s):  
Michael Ying Yang ◽  
Wentong Liao ◽  
Yanpeng Cao ◽  
Bodo Rosenhahn
Keyword(s):  
Anomaly Detection ◽  
Gaussian Process ◽  
Dirichlet Process ◽  
Event Recognition ◽  
Process Models ◽  
Hierarchical Dirichlet Process ◽  
Video Event

scholarly journals Spiked Dirichlet Process Priors for Gaussian Process Models

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Terrance Savitsky ◽  
Marina Vannucci
Keyword(s):  
Gaussian Process ◽  
Dirichlet Process ◽  
Continuous Distribution ◽  
Bayesian Variable Selection ◽  
Process Models ◽  
Model Based Clustering ◽  
Posterior Sampling ◽  
Posterior Inference ◽  
Gaussian Process Models ◽  
Prior Models

We expand a framework for Bayesian variable selection for Gaussian process (GP) models by employing spiked Dirichlet process (DP) prior constructions over set partitions containing covariates. Our approach results in a nonparametric treatment of the distribution of the covariance parameters of the GP covariance matrix that in turn induces a clustering of the covariates. We evaluate two prior constructions: the first one employs a mixture of a point-mass and a continuous distribution as the centering distribution for the DP prior, therefore, clustering all covariates. The second one employs a mixture of a spike and a DP prior with a continuous distribution as the centering distribution, which induces clustering of the selected covariates only. DP models borrow information across covariates through model-based clustering. Our simulation results, in particular, show a reduction in posterior sampling variability and, in turn, enhanced prediction performances. In our model formulations, we accomplish posterior inference by employing novel combinations and extensions of existing algorithms for inference with DP prior models and compare performances under the two prior constructions.

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models
Sign in / Sign up

Export Citation Format

Share Document