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Acceleration of the
AbstractThe expectation–maximization (EM) algorithm is a well‐known iterative algorithm for finding maximum likelihood estimates from incomplete data and is used in several statistical models with latent variables and missing data. The algorithm also exhibits a monotonic increase in a likelihood function and satisfies parameter constraints for its ...
Masahiro Kuroda, Zhi Geng
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Acceleration of the EM algorithm
Systems and Computers in Japan, 2000The EM algorithm is used for many applications, including the Boltzmann machine, stochastic Perceptron, and HMM. This algorithm gives an iterating procedure for calculating the MLE of stochastic models which have hidden random variables. It is simple, but the convergence is slow.
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Local-EM and the EMS Algorithm
Journal of Computational and Graphical Statistics, 2011The use of local likelihood methods (Tibshirani and Hastie 1987; Loader 1999) in the presence of data that are either interval or area censored leads naturally to the consideration of EM-type strategies, or rather local-EM algorithms. In this article we consider a class of local-EM algorithms suitable for density or intensity estimation in the temporal
Chun-Po Steve Fan +2 more
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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
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2001
Abstract This chapter introduces the expectation–maximization (EM) algorithm, a powerful iterative method for obtaining maximum likelihood estimates when data are incomplete or latent variables are involved. The algorithm alternates between an E-step, which computes the expected value of the complete-data log-likelihood given current ...
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Abstract This chapter introduces the expectation–maximization (EM) algorithm, a powerful iterative method for obtaining maximum likelihood estimates when data are incomplete or latent variables are involved. The algorithm alternates between an E-step, which computes the expected value of the complete-data log-likelihood given current ...
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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
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Comparison of the EM algorithm and alternatives
Numerical Algorithms, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Theresa Springer, Karsten Urban
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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.
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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.
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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.
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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.
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