Results 241 to 250 of about 688,609 (284)
Some of the next articles are maybe not open access.
EM Algorithm for Segregation Analysis
Biometrical Journal, 1992AbstractWe consider the problem of estimating segregation ratios in families based on ascertainment through affected children, formulate it as an incomplete problem and work out the EM algorithm for maximum likelihood estimation of segregation ratios. We treat both the cases of known and unknown ascertainment probability. We also derive expressions for
Achuthan, N. R., Krishnan, T.
openaire +2 more sources
2001
Abstract Finding maximum likelihood estimates usually requires a numerical method. Classical techniques such as the Newton–Raphson and Gauss–Newton algorithms have been the main tools for this purpose. The motivation for such techniques generally comes from calculus.
openaire +2 more sources
Abstract Finding maximum likelihood estimates usually requires a numerical method. Classical techniques such as the Newton–Raphson and Gauss–Newton algorithms have been the main tools for this purpose. The motivation for such techniques generally comes from calculus.
openaire +2 more sources
Deterministic annealing EM algorithm
Neural Networks, 1998This paper presents a deterministic annealing EM (DAEM) algorithm for maximum likelihood estimation problems to overcome a local maxima problem associated with the conventional EM algorithm. In our approach, a new posterior parameterized by `temperature' is derived by using the principle of maximum entropy and is used for controlling the annealing ...
Naonori Ueda, Ryohei Nakano
openaire +2 more sources
Improving the EM algorithm for mixtures
Statistics and Computing, 1999One of the estimating equations of the Maximum Likelihood Estimation method, for finite mixtures of the one parameter exponential family, is the first moment equation. This can help considerably in reducing the labor and the cost of calculating the Maximum Likelihood estimates.
Dimitris Karlis, Evdokia Xekalaki
openaire +1 more source
Comparison of the EM algorithm and alternatives
Numerical Algorithms, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Theresa Springer, Karsten Urban
openaire +1 more source
2004
Maximum likelihood is the dominant form of estimation in applied statistics. Because closed-form solutions to likelihood equations are the exception rather than the rule, numerical methods for finding maximum likelihood estimates are of paramount importance.
openaire +1 more source
Maximum likelihood is the dominant form of estimation in applied statistics. Because closed-form solutions to likelihood equations are the exception rather than the rule, numerical methods for finding maximum likelihood estimates are of paramount importance.
openaire +1 more source
1991
In the previous chapters, we examined various methods which are applied directly to the likelihood or to the posterior density. In this and the following chapters, we examine the data augmentation algorithms, including the EM algorithm, the data augmentation algorithm and the Gibbs sampler.
openaire +1 more source
In the previous chapters, we examined various methods which are applied directly to the likelihood or to the posterior density. In this and the following chapters, we examine the data augmentation algorithms, including the EM algorithm, the data augmentation algorithm and the Gibbs sampler.
openaire +1 more source
The EM algorithm in medical imaging
Statistical Methods in Medical Research, 1997This article outlines the statistical developments that have taken place in the use of the EM algorithm in emission and transmission tomography during the past decade or so. We discuss the statistical aspects of the modelling of the projection data for both the emission and transmission cases and define the relevant probability models.
openaire +2 more sources