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Gaussian process

Known as: GP, Gaussian Processes, Gaussian stochastic process 
In probability theory and statistics, a Gaussian process is a statistical model where observations occur in a continuous domain, e.g. time or space… 
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Related topics

Related topics

34 relations
Additive white Gaussian noiseAutoregressive modelBayesian interpretation of kernel regularizationBrownian motion

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2012
Review
2012
A Study of the Fourth-Order Small Perturbation Method for Scattering From Two-Layer Rough Surfaces
Predictions of the fourth-order small perturbation method (SPM) are examined for scattering from two rough surfaces in a layered… 
2010
2010
Frame detection and timing acquisition for OFDM transmissions with unknown interference
Frame detection and timing acquisition are challenging tasks in orthogonal frequency-division multiplexing systems plagued by… 
2009
2009
Bayesian Localized Multiple Kernel Learning
Multiple kernel learning approaches to multi-view learning [1, 11, 7] have recently become very popular since they can easily… 
2008
2008
A Bayesian approach to empirical local linearization for robotics
Local linearizations are ubiquitous in the control of robotic systems. Analytical methods, if available, can be used to obtain… 
Highly Cited
2005
Highly Cited
2005
Confidentiality performance of spectral-phase-encoded optical CDMA
Enhanced security has often been cited as an important benefit of optical code-division multiple-access (O-CDMA) signaling but… 
Highly Cited
1998
Highly Cited
1998
A dynamic index for managed currencies funds using CME currency contracts
The goal of this paper is to equip the investor with the tools and understanding necessary to evaluate managed currencies… 
Highly Cited
1997
Highly Cited
1997
Time scale analysis of an ATM queueing system with long-range dependent traffic
Several types of network traffic have been shown to exhibit long-range dependence (LRD). In this work, we show that the busy… 
Highly Cited
1995
Highly Cited
1995
Bayesian Learning for Neural Networks
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions… 
Highly Cited
1970
Highly Cited
1970
Likelihood ratios for Gaussian processes
We give a comprehensive discussion of the structure of the likelihood ratio (LR) for discrimination between two Gaussian… 
1965
1965
The distribution of intervals between zero crossings of sine wave plus random noise and allied topics
Using the first term of a series given by Longuet-Higgins [5] for the general case an approximate solution is found in the form… 
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