Displaying similar documents to “The EM algorithm and its implementation for the estimation of frequencies of SNP-haplotypes”

An application of the expectation-maximization algorithm to interference rejection for direct-sequence spread-spectrum signals

Quan G. Zhang, Costas N. Georghiades (1999)

Kybernetika

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For a direct-sequence spread-spectrum (DS-SS) system we pose and solve the problem of maximum-likelihood (ML) sequence estimation in the presence of narrowband interference, using the expectation-maximization (EM) algorithm. It is seen that the iterative EM algorithm obtains at each iteration an estimate of the interference which is then subtracted from the data before a new sequence estimate is produced. Both uncoded and trellis coded systems are studied, and the EM-based algorithm...

On EM algorithms and their proximal generalizations

Stéphane Chrétien, Alfred O. Hero (2008)

ESAIM: Probability and Statistics

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In this paper, we analyze the celebrated EM algorithm from the point of view of proximal point algorithms. More precisely, we study a new type of generalization of the EM procedure introduced in [Chretien and Hero (1998)] and called Kullback-proximal algorithms. The proximal framework allows us to prove new results concerning the cluster points. An essential contribution is a detailed analysis of the case where some cluster points lie on the boundary of the parameter space. ...

Em Algorithm for MLE of a Probit Model for Multiple Ordinal Outcomes

Grigorova, Denitsa, Encheva, Elitsa, Gueorguieva, Ralitza (2013)

Serdica Journal of Computing

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The correlated probit model is frequently used for multiple ordered data since it allows to incorporate seamlessly different correlation structures. The estimation of the probit model parameters based on direct maximization of the limited information maximum likelihood is a numerically intensive procedure. We propose an extension of the EM algorithm for obtaining maximum likelihood estimates for a correlated probit model for multiple ordinal outcomes. The algorithm is implemented in...

Fuzzy clustering of spatial binary data

Mô Dang, Gérard Govaert (1998)

Kybernetika

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An iterative fuzzy clustering method is proposed to partition a set of multivariate binary observation vectors located at neighboring geographic sites. The method described here applies in a binary setup a recently proposed algorithm, called Neighborhood EM, which seeks a partition that is both well clustered in the feature space and spatially regular [AmbroiseNEM1996]. This approach is derived from the EM algorithm applied to mixture models [Dempster1977], viewed as an alternate optimization...