Nonparametric Mixtures of Gaussian Processes With Power-Law Behavior

Gaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches, based on a particularly effective method for placing a prior distribution over the space of regression functions. Several researchers have considered postulating mixtures of GPs as a means of dealing...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2012-12, Vol.23 (12), p.1862-1871
Main Authors: Chatzis, S. P., Demiris, Y.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Bayesian analysis
Bayesian methods
Computer science
control theory
systems
Discontinuity
Effectiveness
Exact sciences and technology
Gating and risering
Gaussian
Gaussian process
Gaussian processes
Inference algorithms
Learning and adaptive systems
Linear inference, regression
Mathematical models
Mathematics
mixture model
Neural networks
Pitman-Yor process
Probability and statistics
Random variables
Regression
Sciences and techniques of general use
Statistics
Studies
Training
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Summary:Gaussian processes (GPs) constitute one of the most important Bayesian machine learning approaches, based on a particularly effective method for placing a prior distribution over the space of regression functions. Several researchers have considered postulating mixtures of GPs as a means of dealing with nonstationary covariance functions, discontinuities, multimodality, and overlapping output signals. In existing works, mixtures of GPs are based on the introduction of a gating function defined over the space of model input variables. This way, each postulated mixture component GP is effectively restricted in a limited subset of the input space. In this paper, we follow a different approach. We consider a fully generative nonparametric Bayesian model with power-law behavior, generating GPs over the whole input space of the learned task. We provide an efficient algorithm for model inference, based on the variational Bayesian framework, and prove its efficacy using benchmark and real-world datasets.
ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2012.2217986