Results 241 to 250 of about 1,626,050 (291)

Probabilistic Methods: Maximum Likelihood

2015
Probabilistic methods for phylogeny aim at ranking trees according to the likelihood of observing the data (i.e. the multiple sequence alignment) given the topology of the tree. In order to compute the probability, the probabilistic tree construction methods estimate P(x |T,t). Here the data is the set of n sequences (taxa), T is the tree and t denotes
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Iterative continuous maximum‐likelihood reconstruction method

Mathematical Methods in the Applied Sciences, 1992
AbstractIn this paper we continue our studying of the iterative maximum‐likelihood reconstruction method. We consider only the continuous case and show some convergence properties of the algorithm. In the discrete case convergence has already been proved.
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On the Method of Maximum Likelihood

Journal of the Royal Statistical Society, 1940
Not ...
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Maximum Likelihood Methods I

1994
In dealing with the problem of estimating the parameters of a structural system of equations we had not, in previous chapters, explicitly stated the form of the density of the random terms appearing in the system. Indeed, the estimation aspects of classical least squares techniques and their generalization to systems of equations are distribution free,
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Maximum-Likelihood Methods for Phylogeny Estimation

2005
Maximum-likelihood (ML) estimation of phylogenies has reached a rather high level of sophistication because of algorithmic advances, improvements in models of sequence evolution, and improvements in statistical approaches and application of cluster computing.
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Simulated Maximum Likelihood, Pseudo‐Maximum Likelihood, and Nonlinear Least Squares Methods

1997
AbstractThe simulated analogues to Maximum Likelihood, Pseudo‐Maximum Likelihood, and Non‐Linear Least Squares Methods are presented. Their asymptotic properties and bias corrections are given under various assumptions. Several kinds of simulators are explored and, among them, simulations based on conditioning, on EM algorithm, or on importance ...
Christian Gouriéroux, Alain Monfort
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Die Maximum-Likelihood-Methode

1970
Die Verteilung der Zufallsvariablen X hange von einem unbekannten Parameter θ ab. θ gehore zu einer Menge Ω von Parametern.- Die Wahrscheinlichkeitsdichte f(x,θ) ist eine Funktion mit dem Stichprobenraum ae als Definitionsmenge und ℝ als Wertmenge. Fast man sie aber bei festgehaltenem x als Funktion mit der Parametermenge Ω als Definitionsmenge auf, so
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Bayes and Maximum Likelihood Methods

2010
While the parameter estimation methods presented so far assumed that the parameters θ and the observations of the output y are deterministic values, the parameters themselves and/or the output will now be seen in a stochastic view as a series of random variables.
Rolf Isermann, Marco Münchhof
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Phylogenetic Inference: Maximum Likelihood Methods

2000
Abstract The idea of using a maximum likelihood (ML) method for phylogenetic inference was first presented by Cavalli-Sforza and Edwards (1967) for gene frequency data, but they encountered a number of problems in implementing the method.
Masatoshi Nei, Sudhir Kumar
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Methods for maximum-likelihood deconvolution

Journal of the Optical Society of America A, 1996
An alternative approach to the maximum-likelihood solution of deconvolution problems is presented. The resulting algorithms are faster converging than the conventional Richardson–Lucy and clean algorithms, as well as being more flexible when one is dealing with different types of noise.
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