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Stochastic models
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81	Sampling for Approximate Inference in Continuous Time Bayesian Networks Yu Fan Christian R. Shelton Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2011-01-19 19:25:42 Monte Carlo methods Stochastic simulation Estimation theory Robot control Markov models Importance sampling Particle filter Probability distribution Bayesian network Exponential distribution Rejection sampling Expectationmaximization algorithm
82	Factored Filtering of Continuous-Time Systems E. Busra Celikkaya Christian R. Shelton University of California, Riverside University of California, Riverside Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2011-06-23 16:38:35 Markov models Markov processes KullbackLeibler divergence Statistical theory Thermodynamics Probability distributions Uniformization Expectationmaximization algorithm Markov chain Bayesian network Stochastic matrix
83	FROM RANDOM MATRICES TO STOCHASTIC OPERATORS ALAN EDELMAN AND BRIAN D. SUTTON Abstract. We propose that classical random matrix models are properly viewed as finite difference schemes for stochastic differential operator Add to Reading List Source URL: faculty.rmc.edu Language: English - Date: 2007-08-01 16:12:27 Mathematical physics Matrices Random matrices Singular value decomposition Linear algebra Random matrix Matrix Alan Edelman Eigenvalues and eigenvectors Hermitian matrix Symmetric matrix Singular value
84	Multi-state models Model diagnostics and model extensions Andrew Titman Lancaster University Add to Reading List Source URL: www.maths.lancs.ac.uk Language: English - Date: 2008-11-28 06:54:22 Markov models Stochastic processes Reliability engineering Survival analysis Senescence Censoring
85	Priors from General Equilibrium Models for VARs by Marco del Negro and Frank Schorfheide Presenter: Keith O’Hara March 10, 2014 Add to Reading List Source URL: borovicka.org Language: English - Date: 2015-05-14 19:54:01 Dynamic stochastic general equilibrium Macroeconomic model Vector autoregression Vars
86	Nonparametric Regression For Locally Stationary Time Series Michael Vogt* University of Cambridge July 27, 2012 Add to Reading List Source URL: www.statistik.uni-bonn.de Language: English - Date: 2015-10-23 03:49:13 Stochastic processes Control theory Time series models Normal distribution Distribution Mixing State-space representation Heat equation Errors-in-variables models
87	A Tensor Spectral Approach to Learning Mixed Membership Community Models arXiv:1302.2684v4 [cs.LG] 24 OctAnima Anandkumar1 , Rong Ge2 , Daniel Hsu3 , and Sham M. Kakade3 Add to Reading List Source URL: arxiv.org Language: English - Date: 2013-10-27 21:56:48 Networks Graph theory Probability distributions Machine learning Statistical models Community structure Dirichlet distribution Higher-order singular value decomposition Stochastic block model Gibbs sampling Markov chain Tensor
88	Bio-PEPA for epidemiological models Jane Hillston. LFCS and CSBE, The University of Edinburgh 24th September 2009 Joint work with Federica Ciocchetta. Add to Reading List Source URL: homepages.inf.ed.ac.uk Language: English - Date: 2009-10-09 05:09:45 PEPA Process calculi Jane Hillston Process calculus Scientific modelling Stochastic
89	Wieland-Wolters-Revised-May19-2010.dvi Add to Reading List Source URL: www.monfispol.eu Language: English - Date: 2011-11-16 06:33:07 New classical macroeconomics Economic forecasting Econometrics Time series models Macroeconomic model Economy of the United States Dynamic stochastic general equilibrium Macroeconomics Survey of Professional Forecasters Greenbook Economic model Vector autoregression
90	Journal of Machine Learning Research2140 Submitted 3/09; Revised 6/10; Published 8/10 Importance Sampling for Continuous Time Bayesian Networks Yu Fan Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2011-01-19 19:25:14 Markov models Probability distributions Graphical models Monte Carlo methods Stochastic simulation Bayesian network Markov chain Importance sampling Gibbs sampling Exponential distribution Cumulative distribution function Variance

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