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Importance sampling
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#	Item
31	Bayesian Inference and the Parametric Bootstrap Bradley Efron Stanford University Importance Sampling for Bayes Posterior Distribution Add to Reading List Source URL: statweb.stanford.edu - Date: 2012-07-10 12:42:28
32	GLOBAL OPTIMISATION OF NEURAL NETWORK MODELS VIA SEQUENTIAL SAMPLING-IMPORTANCE RESAMPLING Jo˜ ao F.G. de Freitas Sue E. Johnson Add to Reading List Source URL: mi.eng.cam.ac.uk - Date: 2000-04-10 07:41:16
33	Randomized Algorithms 2013A Lecture 11 – Importance Sampling, and the Local Lemma∗ Robert Krauthgamer 1 Add to Reading List Source URL: www.wisdom.weizmann.ac.il Language: English - Date: 2013-01-21 12:15:37
34	Policy Improvement for POMDPs using Normalized Importance Sampling Christian R. Shelton Artificial Intelligence Lab Massachusetts Institute of Technology Add to Reading List Source URL: www.cs.ucr.edu Language: English - Date: 2003-05-27 20:49:12
35	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: www.cs.ucr.edu Language: English - Date: 2010-09-07 12:44:27
36	Sequential Monte Carlo Methods for Statistical Analysis of Tables Yuguo C HEN, Persi D IACONIS, Susan P. H OLMES, and Jun S. L IU We describe a sequential importance sampling (SIS) procedure for analyzing two-way zero– Add to Reading List Source URL: statweb.stanford.edu Language: English - Date: 2005-05-31 19:32:38
37	On improved estimation for importance sampling Add to Reading List Source URL: www2.warwick.ac.uk Language: English - Date: 2011-09-05 08:40:13
38	Q IWA Publishing 2006 Journal of Hydroinformatics \| 08.3 \| Groundwater pollution risk using a modified Latin hypercube sampling Add to Reading List Source URL: www.iwaponline.com Language: English - Date: 2006-06-12 11:39:02 Importance sampling Variance reduction Variance Latin hypercube sampling Hydrogeology Normal distribution Estimation theory Sensitivity analysis Standard error Statistics Statistical inference Data analysis
39	Importance Sampling Estimates for Policies with Memory Christian R. Shelton MIT, AI Lab 545 Technology Square, NE43-741, Cambridge, MAUSA Abstract Add to Reading List Source URL: www.cs.ucr.edu Language: English - Date: 2003-05-27 20:51:52 Monte Carlo methods Computational statistics Importance sampling Variance reduction Pi Logarithm Hidden Markov model Reinforcement learning Markov models Mathematical analysis Mathematics
40	A Hierarchical Bayes based approach to ensemble-variational data assimilation M Tsyrulnikov and A Rakitko Motivation Add to Reading List Source URL: srnwp.met.hu Language: English - Date: 2014-10-27 11:08:29 Data analysis Monte Carlo methods Covariance and correlation Ensemble Kalman filter Covariance matrix Covariance Data assimilation Normal distribution Importance sampling Statistics Estimation theory Bayesian statistics

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