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Biometrics, 1993
Summary: A modification of the EM algorithm is proposed for situations in which the maximization of the ``complete data'' likelihood function does not have a closed-form solution. Self-consistency of the modified algorithm is established. Application to carcinogenicity experiments is illustrated, and the results of a simulation study comparing the ...
Rai, S. N., Matthews, D. E.
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Summary: A modification of the EM algorithm is proposed for situations in which the maximization of the ``complete data'' likelihood function does not have a closed-form solution. Self-consistency of the modified algorithm is established. Application to carcinogenicity experiments is illustrated, and the results of a simulation study comparing the ...
Rai, S. N., Matthews, D. E.
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Proceedings of the IEEE, 1988
The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that may be useful in accelerating the algorithm and in simplifying the computations involved. The extension works with an intermediate complete data specification, and
Mordechai Segal, Ehud Weinstein
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The estimate-maximize (EM) algorithm is an iterative method for finding maximum-likelihood parameter estimates from incomplete data. The authors develop an extension of the EM algorithm that may be useful in accelerating the algorithm and in simplifying the computations involved. The extension works with an intermediate complete data specification, and
Mordechai Segal, Ehud Weinstein
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EM*: An EM Algorithm for Big Data
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2016Existing data mining techniques, more particularly iterative learning algorithms, become overwhelmed with big data. While parallelism is an obvious and, usually, necessary strategy, we observe that both (1) continually revisiting data and (2) visiting all data are two of the most prominent problems especially for iterative, unsupervised algorithms like
Hasan Kurban +2 more
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An EM Algorithm for Capsule Regression
Neural Computation, 2021We investigate a latent variable model for multinomial classification inspired by recent capsule architectures for visual object recognition (Sabour, Frosst, & Hinton, 2017 ). Capsule architectures use vectors of hidden unit activities to encode the pose of visual objects in an image, and they use the lengths of these vectors to encode the ...
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Acceleration of the EM algorithm: P-EM versus epsilon algorithm
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alain F. Berlinet, Christophe Roland
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Statistics and Computing, 2013
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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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