
Estimating Gaussian mixtures using sparse polynomial moment systems
The method of moments is a statistical technique for density estimation ...
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Markov Equivalence of MaxLinear Bayesian Networks
Maxlinear Bayesian networks have emerged as highly applicable models fo...
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Computing Maximum Likelihood Estimates for Gaussian Graphical Models with Macaulay2
We introduce the package GraphicalModelsMLE for computing the maximum li...
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Toric invariant theory for maximum likelihood estimation in loglinear models
We establish connections between invariant theory and maximum likelihood...
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Likelihood Geometry of Correlation Models
Correlation matrices are standardized covariance matrices. They form an ...
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The Maximum Likelihood Degree of Linear Spaces of Symmetric Matrices
We study multivariate Gaussian models that are described by linear condi...
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Structure Learning for Cyclic Linear Causal Models
We consider the problem of structure learning for linear causal models b...
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Invariant theory and scaling algorithms for maximum likelihood estimation
We show that maximum likelihood estimation in statistics is equivalent t...
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Conditional Independence in Maxlinear Bayesian Networks
Motivated by extreme value theory, maxlinear Bayesian networks have bee...
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Maximum Likelihood Estimation of Toric Fano Varieties
We study the maximum likelihood estimation problem for several classes o...
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Moment Identifiability of Homoscedastic Gaussian Mixtures
We consider the problem of identifying a mixture of Gaussian distributio...
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Autocovariance Varieties of Moving Average Random Fields
We study the autocovariance functions of moving average random fields ov...
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Discrete Gaussian distributions via theta functions
We introduce a discrete analogue of the classical multivariate Gaussian ...
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Carlos Améndola
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