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Gibbs algorithm
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1	Numerical Algorithms 26: 77–92, 2001.  2001 Kluwer Academic Publishers. Printed in the Netherlands. A Padé-based algorithm for overcoming the Gibbs phenomenon Tobin A. Driscoll a,∗ and Bengt Fornberg b,∗∗ Add to Reading List Source URL: www.math.udel.edu Language: English - Date: 2013-06-20 09:28:44
2	Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes E. Busra Celikkaya University of California, Riverside CELIKKAE @ CS . UCR . EDU Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2015-05-14 15:50:46 Graphical models Statistics Applied mathematics Mathematics Estimation theory Bayesian network Networks Gibbs sampling Belief propagation Expectationmaximization algorithm Computation tree
3	Learning Continuous-Time Social Network Dynamics Yu Fan University of California, Riverside Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2011-01-19 19:25:15 Statistics Estimation theory Markov models Statistical theory Statistical models Regression analysis Bayesian network Gibbs sampling Markov chain Expectationmaximization algorithm Parameter Mathematical model
4	Robust Entity Clustering via Phylogenetic Inference Nicholas Andrews and Jason Eisner and Mark Dredze Department of Computer Science and Human Language Technology Center of Excellence Johns Hopkins University 3400 N. Cha Add to Reading List Source URL: www.cs.jhu.edu Language: English - Date: 2014-05-16 20:46:24 Statistics Estimation theory Computational statistics Cluster analysis Gibbs sampling Markov chain Monte Carlo Expectationmaximization algorithm Consensus clustering Likelihood function Sampling
5	Network Analysis and Modeling, CSCI 5352 LectureProf. Aaron Clauset Add to Reading List Source URL: tuvalu.santafe.edu Language: English - Date: 2013-11-23 11:09:20 Statistics Statistical theory Monte Carlo methods Markov chain Monte Carlo Markov models Stochastic simulation Estimation theory MetropolisHastings algorithm Gibbs sampling Markov chain Maximum likelihood estimation Importance sampling
6	2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) INDEPENDENT DOUBLY ADAPTIVE REJECTION METROPOLIS SAMPLING Luca Martino? , Jesse Read† , David Luengo‡ ? Add to Reading List Source URL: users.ics.aalto.fi Language: English - Date: 2014-08-22 09:13:11 Statistics Monte Carlo methods Computational statistics Markov chain Monte Carlo Statistical theory Non-uniform random numbers Rejection sampling MetropolisHastings algorithm Stochastic simulation Gibbs sampling
7	Slice sampling C. Planas, and A. Rossi Joint Research Centre of European Commission MACFINROBODS WorkshopJanuary 2015, London Add to Reading List Source URL: www.macfinrobods.eu Language: English - Date: 2015-02-13 10:28:08 Statistics Statistical theory Markov chain Monte Carlo Bayesian statistics Estimation theory Monte Carlo methods Computational statistics Slice sampling Gibbs sampling Likelihood function MetropolisHastings algorithm
8	UNIVERSITY OF CALIFORNIA RIVERSIDE Continuous Time Bayesian Network Approximate Inference and Social Network Applications Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2011-01-19 19:25:15 Monte Carlo methods Stochastic simulation Computational statistics Markov models Markov chain Monte Carlo Bayesian network Gibbs sampling Particle filter Approximate inference Expectationmaximization algorithm Statistics Importance sampling
9	2013 IEEE International Conference on Computer Vision Slice Sampling Particle Belief Propagation Oliver Müller, Michael Ying Yang, and Bodo Rosenhahn Institute for Information Processing (TNT), Leibniz University Hanno Add to Reading List Source URL: www.tnt.uni-hannover.de Language: English - Date: 2016-08-02 06:36:10 Monte Carlo methods Markov chain Monte Carlo Non-uniform random numbers Probability distributions Computational statistics MetropolisHastings algorithm Rejection sampling Gibbs sampling Normal distribution Slice sampling Uniform distribution
10	A Level-set Hit-and-run Sampler for Quasi-Concave Distributions Shane T. Jensen and Dean P. Foster Department of Statistics, The Wharton School, University of Pennsylvania Abstract Add to Reading List Source URL: www-stat.wharton.upenn.edu Language: English - Date: 2014-05-24 19:58:02 Monte Carlo methods Markov chain Monte Carlo Non-uniform random numbers Probability distributions Computational statistics Gibbs sampling Rejection sampling MetropolisHastings algorithm Slice sampling Bayesian network Quasiconvex function Normal distribution

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