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Graphical model
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11	In this talk, I will introduce hinge-loss Markov random fields (HLMRFs), a new kind of probabilistic graphical model that supports scalable collective inference from richly structured data. HL-MRFs unify three different Add to Reading List Source URL: mmds-data.org - Date: 2016-06-23 15:50:48
12	A Graphical Model for Recognizing Sung Melodies Christopher Raphael School of Informatics Indiana Univ. Bloomington, IN 47408 Add to Reading List Source URL: music.informatics.indiana.edu - Date: 2005-07-13 13:07:32
13	Several problems in applied mathematics and statistics require integrating a function f over a high-dimensional domain. For example, estimating the partition function of a graphical model for a fixed set of parameters re Add to Reading List Source URL: mmds-data.org Language: English - Date: 2016-06-23 15:50:48
14	Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2008-08-08 22:07:31 Statistics Probability Statistical theory Probability distributions Statistical models Bayesian statistics Stochastic processes Graphical models Bayesian network Gibbs sampling Dirichlet process Mixture model
15	Mean Field Variational Approximations in Continuous-Time Markov Processes A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Add to Reading List Source URL: www.cs.huji.ac.il Language: English - Date: 2015-08-10 08:23:20 Bayesian statistics Statistics Graphical models Probability Markov models Bayesian network Probability distribution Markov chain Expectation propagation Approximate inference Likelihood function Hidden Markov model
16	C:/usr/ztu/Document/paper/conference/2005/iccv_05/Gsw/gsw_final.dvi Add to Reading List Source URL: pages.ucsd.edu Language: English - Date: 2005-07-22 13:00:04 Statistical models Artificial intelligence Statistics Statistical theory Bayesian statistics Image segmentation Graphical model Discriminative model Market segmentation Belief propagation Conference on Computer Vision and Pattern Recognition Graph cuts in computer vision
17	Learning Symmetric Relational Markov Random Fields A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science by Add to Reading List Source URL: www.cs.huji.ac.il Language: English - Date: 2015-08-10 08:23:33 Graphical models Statistics Probability Statistical theory Variable elimination Markov random field Markov chain Belief propagation Conceptual model Bayesian network Deep learning
18	Hierarchical factor item response theory models for PIRLS: capturing clustering effects at multiple levels 1 Frank Rijmen Add to Reading List Source URL: ierinstitute.org Language: English - Date: 2012-01-04 07:01:18 Statistics Psychometrics Statistical models Educational psychology Educational research Psychological testing Item response theory Progress in International Reading Literacy Study Latent variable Factor analysis Graphical model Expectationmaximization algorithm
19	Univ. of Pittsburgh Conditional Random Fields Add to Reading List Source URL: people.cs.pitt.edu Language: English - Date: 2014-10-25 10:56:22 Graphical models Machine learning Statistical models Learning Artificial intelligence Statistics Conditional random field Generative model Markov random field Discriminative model Bayesian network Factor graph