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Expectation–maximization algorithm
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#	Item
691	INVESTIGATION A Novel Approach to Estimating Heterozygosity from Low-Coverage Genome Sequence Katarzyna Bryc,,1 Nick Patterson,† and David Reich Add to Reading List Source URL: genetics.med.harvard.edu Language: English - Date: 2013-12-03 11:03:23 Molecular biology Genetic mapping DNA sequencing Zygosity Shotgun sequencing Genomes Project Reference genome Genotype Expectation–maximization algorithm Biology Genetics Genomics
692	Stochastic Link and Group Detection Jeremy Kubica Andrew Moore Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2002-09-06 13:10:46 Statistical models Network theory Cluster analysis Link analysis Mixture model Expectation–maximization algorithm Naive Bayes classifier RANSAC Database Statistics Machine learning Estimation theory
693	PARAMETER IDENTIFICATION VIA THE ADJOINT METHOD: APPLICATION TO PROTEIN REGULATORY NETWORKS Robin L. Raffard ∗ Keith Amonlirdviman ∗∗ Jeffrey D. Axelrod ∗∗∗ Claire J. Tomlin ∗∗∗∗ Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2007-01-10 19:58:34 M-estimators Maximum likelihood Dimensional analysis Partial differential equation Polar coordinate system Expectation–maximization algorithm Statistics Estimation theory Statistical theory
694	Unsupervised Joint Alignment and Clustering using Bayesian Nonparametrics Marwan A. Mattar Allen R. Hanson Department of Computer Science Add to Reading List Source URL: vis-www.cs.umass.edu Language: English - Date: 2012-09-09 18:22:40 Cluster analysis Statistical models K-means clustering Mixture model Expectation–maximization algorithm Dirichlet process Chinese restaurant process Pattern recognition Normal distribution Statistics Machine learning Stochastic processes
695	American Political Science Review Vol. 95, No. 1 March 2001 Add to Reading List Source URL: gking.harvard.edu Language: English - Date: 2013-01-10 13:44:32 Econometrics Estimation theory Listwise deletion Regression analysis Imputation Resampling Expectation–maximization algorithm Linear regression Variance Statistics Missing data Data analysis
696	Gaussian mixture models and the EM algorithm Ramesh Sridharan∗ These notes give a short introduction to Gaussian mixture models (GMMs) and the Expectation-Maximization (EM) algorithm, first for the specific case of GMM Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2014-02-05 10:25:35 Expectation–maximization algorithm Normal distribution Standard deviation Kullback–Leibler divergence Likelihood function Generalized method of moments Statistics Estimation theory Maximum likelihood
697	Online Polylingual Topic Models for Fast Document Translation Detection Kriste Krstovski School of Computer Science University of Massachusetts Amherst Amherst, MA, [removed]removed] Add to Reading List Source URL: aclweb.org Language: English - Date: 2013-07-25 09:32:19 Latent Dirichlet allocation Topic model Kullback–Leibler divergence Gibbs sampling Hellinger distance Variational Bayesian methods Probabilistic latent semantic analysis Expectation–maximization algorithm Information retrieval Statistics Statistical natural language processing Dynamic topic model
698	LEARNING TO ALIGN POLYPHONIC MUSIC Shai Shalev-Shwartz Joseph Keshet Yoram Singer {shais,jkeshet,singer}@cs.huji.ac.il School of Computer Science and Engineering, The Hebrew University, Jerusalem, 91904, Israel ABSTRACT Add to Reading List Source URL: ismir2004.ismir.net Language: English - Date: 2014-06-01 11:42:11 Supervised learning Function Perceptron Dynamic programming Hidden Markov model Expectation–maximization algorithm Pattern recognition Vector space Linear classifier Mathematics Algebra Machine learning
699	T O APPEAR IN N EURAL I NFORMATION P ROCESSING S YSTEMS[removed]Efficient Multiscale Sampling from Products of Gaussian Mixtures Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, and Alan S. Willsky Department of E Add to Reading List Source URL: ssg.mit.edu Language: English - Date: 2012-02-01 13:34:22 Computational statistics Estimation theory Gaussian function Gibbs sampling Normal distribution Expectation–maximization algorithm Kullback–Leibler divergence Multivariate kernel density estimation Importance sampling Statistics Monte Carlo methods Non-parametric statistics
700	Reducing Reliance on Relevance Judgments for System Comparison by Using Expectation-Maximization Ning Gao,1 William Webber2 and Douglas W. Oard1 1 College of Information Studies/UMIACS, University of Maryland, College P Add to Reading List Source URL: www.williamwebber.com Language: English - Date: 2014-06-15 18:36:15 Statistics Natural language processing Computational linguistics Text Retrieval Conference Relevance Precision and recall Expectation–maximization algorithm Judgment Relevance feedback Information science Information retrieval Science

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