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Estimation of distribution algorithm
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#	Item
11	Hilbert Space Embeddings of Hidden Markov Models Le Song Byron Boots Add to Reading List Source URL: select.cs.cmu.edu Language: English - Date: 2011-07-19 22:00:19 Mathematical analysis Mathematics Algebra Estimation theory Hilbert space Operator theory Markov models Linear algebra Hidden Markov model Normal distribution Expectationmaximization algorithm Speech recognition
12	A SURVEY OF MODEL-BASED METHODS FOR GLOBAL OPTIMIZATION Thomas Bartz-Beielstein SPOTSeven Lab, TH K¨ oln, Gummersbach, Germany. spotseven.de Add to Reading List Source URL: bioma.ijs.si Language: English - Date: 2016-05-13 03:46:36 Mathematical optimization Cybernetics Scientific modeling Evolutionary algorithms Mathematical modeling Surrogate model Global optimization Genetic algorithm DONE Stochastic optimization Simulated annealing Estimation of distribution algorithm
13	Introducing Graphical Models to Analyze Genetic Programming Dynamics Erik Hemberg Constantin Berzan Add to Reading List Source URL: groups.csail.mit.edu Language: English - Date: 2015-12-01 16:11:14 Graphical models Statistical models Statistics Graph theory Computer programming Bayesian network Genetic algorithm Tree Modularity Node Estimation of distribution algorithm Graphical models for protein structure
14	A Distributed Message Passing Algorithm for Sensor Localization Max Welling and Joseph J. Lim Bren School of Information and Computer Science UC Irvine, CA, USA {welling, josephjl}@ics.uci.edu Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2008-06-14 04:41:24 Normal distribution Variance Von Mises distribution Belief propagation Image noise Gamma distribution Mean squared error Wireless sensor network Statistics Probability theory Estimation theory
15	Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets Diederik P. Kingma Max Welling Machine Learning Group, University of Amsterdam Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2014-05-14 15:18:52 Statistical models Statistical theory Statistical inference Expectation–maximization algorithm Bayesian network Gibbs sampling Normal distribution Maximum likelihood Latent variable Statistics Bayesian statistics Estimation theory
16	Predicting Simulation Parameters of Biological Systems using a Gaussian Process Model Xiangxin Zhu, Max Welling Fang Jin, John Lowengrub Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2012-02-01 21:23:55 Statistical theory Statistical models Normal distribution Parametric model Regression analysis Gaussian process Expectation–maximization algorithm Maximum likelihood Statistics Estimation theory Econometrics
17	Maximum-likelihood estimation for the offset normal shape distributions using EM Alfred Kume Institute of Mathematics, Statistics and Actuarial Science, University of Kent Canterbury, CT2 7NF,UK Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2010-09-23 15:44:42 Normal distribution Multivariate normal distribution Complex normal distribution Expectation–maximization algorithm Moment Exponential family Statistics Mathematical analysis Probability and statistics
18	THIS IS A DRAFT VERSION. FINAL VERSION TO BE PUBLISHED AT NIPS ’06 Structure Learning in Markov Random Fields Sridevi Parise Bren School of Information and Computer Science Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2006-09-04 17:32:02 Probability theory Estimation theory Graphical models Marginal likelihood Expectation–maximization algorithm Variational Bayesian methods Normal distribution Maximum likelihood Bayesian network Statistics Bayesian statistics Statistical theory
19	Evidence Estimation for Bayesian Partially Observed MRFs Yutian Chen Department of Computer Science University of California, Irvine Irvine, CA 92697 Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2013-02-14 03:02:58 Normal distribution Expectation–maximization algorithm Variational Bayesian methods Bayesian network Markov chain Monte Carlo Maximum likelihood Deviance information criterion Pierre-Simon Laplace Markov random field Statistics Bayesian statistics Marginal likelihood
20	Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring Sungjin Ahn Dept. of Computer Science, UC Irvine, Irvine, CA, USA SUNGJIA @ ICS . UCI . EDU Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2012-05-22 12:55:14 Fisher information Markov chain Monte Carlo Normal distribution Stochastic gradient descent Maximum a posteriori estimation Confidence interval Central limit theorem Score Expectation–maximization algorithm Statistics Estimation theory Maximum likelihood