Results 1 - 10 of 20,680

Maximum likelihood from incomplete data via the EM algorithm

by A. P. Dempster, N. M. Laird, D. B. Rubin - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract - Cited by 11972 (17 self) - Add to MetaCart
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

ADAPTIVE MICROPHONE ARRAY BASED ON MAXIMUM LIKELIHOOD CRITERION

by Zoran Šarić, Slobodan Jovičić, Srbijanka Turajlić
"... The Minimum Variance (MV) criterion is widely used for weight vector estimation of the adaptive microphone array (AMA). The drawback of this criterion is the cancellation of the desired speech signal and its degradation when the microphone array is in a room with reverberation. Applying the Maximum ..."
Abstract - Add to MetaCart
The Minimum Variance (MV) criterion is widely used for weight vector estimation of the adaptive microphone array (AMA). The drawback of this criterion is the cancellation of the desired speech signal and its degradation when the microphone array is in a room with reverberation. Applying the Maximum

UNDER THE MAXIMUM LIKELIHOOD CRITERION Publication No._____________

by Derrick Joel Zwickl, David M. Hillis, David C. Cannetella, Robin R. Gutell, Robert K. Jansen, Tandy Warnow, Derrick Joel Zwickl, Derrick Joel Zwickl, Ph. D, Supervisor David, M. Hillis , 2006
"... Copyright by ..."
Abstract - Add to MetaCart
Copyright by

Matching Moving Objects by Parts with a Maximum Likelihood Criterion

by Eric Dahai Cheng , Massimo Piccardi
"... Abstract In this paper we present an algorithm for matching the appearance of two moving objects based on a matchingby-parts approach and a maximum likelihood criterion. We assume that the two moving objects to be matched are first extracted from videos by a preliminary foreground extraction-tracki ..."
Abstract - Add to MetaCart
Abstract In this paper we present an algorithm for matching the appearance of two moving objects based on a matchingby-parts approach and a maximum likelihood criterion. We assume that the two moving objects to be matched are first extracted from videos by a preliminary foreground extraction

Factorized Normalized Maximum Likelihood Criterion for Learning Bayesian Network Structures

by Tomi Silander, Teemu Roos, Petri Kontkanen, Petri Myllymäki , 2008
"... This paper introduces a new scoring criterion, factorized normalized maximum likelihood, for learning Bayesian network structures. The proposed scoring criterion requires no parameter tuning, and it is decomposable and asymptotically consistent. We compare the new scoring criterion to other scoring ..."
Abstract - Cited by 13 (4 self) - Add to MetaCart
This paper introduces a new scoring criterion, factorized normalized maximum likelihood, for learning Bayesian network structures. The proposed scoring criterion requires no parameter tuning, and it is decomposable and asymptotically consistent. We compare the new scoring criterion to other scoring

Factorized normalized maximum likelihood criterion for learning bayesian network structures,” Submitted for PGM08

by Tomi Sil, Teemu Roos, Petri Kontkanen, Petri Myllymäki , 2008
"... This paper introduces a new scoring criterion, factorized normalized maximum likelihood, for learning Bayesian network structures. The proposed scoring criterion requires no parameter tuning, and it is decomposable and asymptotically consistent. We compare the new scoring criterion to other scoring ..."
Abstract - Cited by 3 (2 self) - Add to MetaCart
This paper introduces a new scoring criterion, factorized normalized maximum likelihood, for learning Bayesian network structures. The proposed scoring criterion requires no parameter tuning, and it is decomposable and asymptotically consistent. We compare the new scoring criterion to other scoring

Flexible camera calibration by viewing a plane from unknown orientations

by Zhengyou Zhang , 1999
"... We propose a flexible new technique to easily calibrate a camera. It only requires the camera to observe a planar pattern shown at a few (at least two) different orientations. Either the camera or the planar pattern can be freely moved. The motion need not be known. Radial lens distortion is modeled ..."
Abstract - Cited by 511 (7 self) - Add to MetaCart
is modeled. The proposed procedure consists of a closed-form solution, followed by a nonlinear refinement based on the maximum likelihood criterion. Both computer simulation and real data have been used to test the proposed technique, and very good results have been obtained. Compared with classical

Paml 4: Phylogenetic analysis by maximum likelihood

by Ziheng Yang - Mol. Biol. Evol , 2007
"... PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which ..."
Abstract - Cited by 1201 (28 self) - Add to MetaCart
PAML, currently in version 4, is a package of programs for phylogenetic analyses of DNA and protein sequences using maximum likelihood (ML). The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which

A maximum likelihood approach to continuous speech recognition

by Lalit R. Bahl, Frederick Jelinek, Robert, L. Mercer - IEEE Trans. Pattern Anal. Machine Intell , 1983
"... Abstract-Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of sta-tistical models for use in speech recognition. We give special attention to determining the ..."
Abstract - Cited by 477 (9 self) - Add to MetaCart
Abstract-Speech recognition is formulated as a problem of maximum likelihood decoding. This formulation requires statistical models of the speech production process. In this paper, we describe a number of sta-tistical models for use in speech recognition. We give special attention to determining

PAML: a program package for phylogenetic analysis by maximum likelihood

by Ziheng Yang - COMPUT APPL BIOSCI 13:555–556 , 1997
"... PAML, currently in version 1.2, is a package of programs for phylogenetic analyses of DNA and protein sequences using the method of maximum likelihood (ML). The programs can be used for (i) maximum likelihood estimation of evolutionary parameters such as branch lengths in a phylogenetic tree, the tr ..."
Abstract - Cited by 1459 (17 self) - Add to MetaCart
PAML, currently in version 1.2, is a package of programs for phylogenetic analyses of DNA and protein sequences using the method of maximum likelihood (ML). The programs can be used for (i) maximum likelihood estimation of evolutionary parameters such as branch lengths in a phylogenetic tree
Results 1 - 10 of 20,680