Results 261 to 270 of about 13,471,124 (303)
Some of the next articles are maybe not open access.
2006
Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
openaire +2 more sources
Abstract This chapter discusses likelihood calculation for multiple sequences on a phylogenetic tree. As indicated at the end of Chapter 3, this is a natural extension to the parsimony method when we want to incorporate differences in branch lengths and in substitution rates between nucleotides. Likelihood calculation on a tree is also a
openaire +2 more sources
Maximum‐likelihood estimation for the removal method
Canadian Journal of Statistics, 1994AbstractWe prove that the profile log‐likelihood function for the removal method of estimating population size is unimodal. The result is obtained by a variation‐diminishing property of the Laplace transform. An implication of this result is that the likelihood‐ratio confidence region for the population size is always an interval.
openaire +2 more sources
1974
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,
openaire +1 more source
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,
openaire +1 more source
Maximum-Likelihood Methods for Phylogeny Estimation
2005Maximum-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.
openaire +2 more sources
Bayes and Maximum Likelihood Methods
2010While 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
openaire +1 more source
Probabilistic Methods: Maximum Likelihood
2015Probabilistic 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
openaire +1 more source