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An Approximate Iterative Algorithm for Modeling of Non-Gaussian Vectors with Given Marginal Distributions and Covariance Matrix
AbstractA new iterative method for modeling of non-Gaussian random vectors with given marginal distributions and a covariance matrix is proposed in...
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Gaussian kernel with correlated variables for incomplete data
The presence of missing components in incomplete instances precludes a kernel-based model from incorporating partially observed components of...
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Continuous Random Vectors
Having studied discrete random variables, that is, random variables taking their values in a finite or countable set, we now introduce random...
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Gaussian scrolls, Gaussian flags and duality
A projective variety whose Gauss map has positive dimensional fibres corresponds to a special kind of scroll called Gaussian . A Gaussian scroll is a...
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Functional linear non-Gaussian acyclic model for causal discovery
In causal discovery, non-Gaussianity has been used to characterize the complete configuration of a linear non-Gaussian acyclic model (LiNGAM),...
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Decoupling Inequalities and Decoupling Coefficients of Gaussian Processes
We use Brascamp-Lieb’s inequality to obtain new decoupling inequalities for general Gaussian vectors, and in particular for finite stationary...
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Hybrid Gaussian/Non-Gaussian Quality-Related Nonlinear Process Monitoring
In complex industrial processes, the mixed characteristics of Gaussian/non-Gaussian and nonlinear data are a common phenomenon. This process is...
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Learning Networks from Gaussian Graphical Models and Gaussian Free Fields
We investigate the problem of estimating the structure of a weighted network from repeated measurements of a Gaussian graphical model (GGM) on the...
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Gaussian Processes
The Gaussian process as a tool for, predominantly, regression tasks in machine learning has only been growing in popularity over recent years....
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Distribution of the Volume of Weighted Gaussian Simplex
Let X 0 ,..., X l be independent standard Gaussian vectors in ℝ d such that l ≤ d . An explicit formula is obtained for the distribution of the volume of a...
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The Gaussian Free Field
The Gaussian free field is defined in this chapter and the identity in law between the vertex occupation field of loop ensembles of intensity ½ or 1...
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Gaussian Boson Sampling
We introduce the fundamental models for Gaussian boson sampling and the link with the computation of the Hafnian. We show how to compute and train...
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Bayesian Latent Gaussian Models
Bayesian latent Gaussian models are Bayesian hierarchical models that assign Gaussian prior densities to the latent parameters. In this chapter, we...
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Infinite-dimensional distances and divergences between positive definite operators, Gaussian measures, and Gaussian processes
This paper presents a survey of recent results on the generalization of distances and divergences on the set of symmetric, positive definite (SPD)...
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Convex Hulls of Several Multidimensional Gaussian Random Walks
Explicit formulas for the expected volume and expected number of facets of the convex hull of several multidimensional Gaussian random walks are...
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Conditional Gaussian Densities
Gaussian probability distributions are the workhorse in Bayesian scientific computing, providing a well understood subclass of distributions that...
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Gaussian Processes
In the previous chapter, we covered the derivation of the posterior distribution for parameter θ as well as the predictive posterior distribution of...
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Universal Gaussian elimination hardware for cryptographic purposes
In this paper, we investigate the possibility of performing Gaussian elimination for arbitrary binary matrices on hardware. In particular, we...
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Gaussian Process Based Photometric Stereo
The performance of non-contact optical measurement like structure light could be heavily influenced by the widespread non-Lambertian highlight...
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Moving objects detection in thermal scene videos using unsupervised Bayesian classifier with bootstrap Gaussian expectation maximization algorithm
In this paper, a new algorithm for moving object detection is proposed by using unsupervised Bayesian classifier with bootstrap Gaussian expectation...
