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Approximate inference
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1	In: Proc. International Symposium of Robotics Research (ISRR), Siestre-Levante, ItalyAn Approximate Inference Approach to Temporal Optimization for Robotics Konrad Rawlik, Dmitry Zarubin, Marc Toussaint, and Se Add to Reading List Source URL: ipvs.informatik.uni-stuttgart.de Language: English - Date: 2017-10-01 09:42:38
2	Switch-Reset Models : Exact and Approximate Inference Chris Bracegirdle David Barber University College London, Gower Street, London UK {c.bracegirdle, d.barber} @cs.ucl.ac.uk Add to Reading List Source URL: web4.cs.ucl.ac.uk Language: English - Date: 2011-03-29 07:20:45
3	Chapter 2 Graphical models and approximate posterior inference In this chapter we review latent variable graphical models and exponential families. We discuss variational methods and Gibbs sampling for approximate poster Add to Reading List Source URL: www.cs.princeton.edu - Date: 2006-03-11 08:59:30
4	Journal of Machine Learning Research–1704 Submitted 8/05; PublishedAssessing Approximate Inference for Binary Gaussian Process Classification Add to Reading List Source URL: www.jmlr.org - Date: 2005-10-02 10:31:59
5	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
6	Approximate Bayesian Computation for Granular and Molecular Dynamics Simulations Lina Kulakova Computational Science and Engineering Laboratory ETH Zürich Add to Reading List Source URL: www.cse-lab.ethz.ch Language: English - Date: 2016-06-15 20:10:57 Statistics Statistical theory Bayesian statistics Estimation theory Probability distributions Statistical models Uncertainty quantification Bayesian network Bayesian inference Sufficient statistic Likelihood function Normal distribution
7	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
8	UNIVERSITY OF CALIFORNIA RIVERSIDE Multivariate Continuous-Time Models: Approximate Inference Algorithms and Medical Informatics Applications Add to Reading List Source URL: rlair.cs.ucr.edu Language: English - Date: 2016-03-21 12:22:45 Stochastic processes Graphical models Markov processes Bayesian network Networks Uniformization Probability distribution Algorithm Statistical inference Approximate inference Statistics Matrix
9	Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems Sameer Agarwal† , Henry Milner† , Ariel Kleiner† , Ameet Talwalkar† , Michael Jordan† , Samuel Madden‡ , Barzan Moz Add to Reading List Source URL: web.eecs.umich.edu Language: English - Date: 2015-03-27 17:54:39 Statistical inference Computational statistics Summary statistics Sampling Estimation theory Bootstrapping Resampling Confidence interval Statistics Sample size determination M-estimator Mode
10	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