
Invariant theory and scaling algorithms for maximum likelihood estimation
We show that maximum likelihood estimation in statistics is equivalent t...
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Maximum Likelihood Estimation from a Tropical and a Bernstein–Sato Perspective
In this article, we investigate Maximum Likelihood Estimation with tools...
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Maximum likelihood estimation for discrete exponential families and random graphs
We characterize the existence of maximum likelihood estimators for discr...
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Maximum Likelihood Estimation of Sparse Networks with Missing Observations
Estimating the matrix of connections probabilities is one of the key que...
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Reciprocal Maximum Likelihood Degrees of Brownian Motion Tree Models
We give an explicit formula for the reciprocal maximum likelihood degree...
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A Scheme for Molecular Computation of Maximum Likelihood Estimators for LogLinear Models
We propose a novel molecular computing scheme for statistical inference....
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JensenShannon Divergence as a GoodnessofFit Measure for Maximum Likelihood Estimation and Curve Fitting
The coefficient of determination, known as R^2, is commonly used as a go...
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Toric invariant theory for maximum likelihood estimation in loglinear models
We establish connections between invariant theory and maximum likelihood estimation for discrete statistical models. We show that norm minimization over a torus orbit is equivalent to maximum likelihood estimation in loglinear models. We use notions of stability under a torus action to characterize the existence of the maximum likelihood estimate, and discuss connections to scaling algorithms.
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