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Importance sampling
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91	Sampling Methods to Estimate Foliage and Other Characteristics of Individual Trees Author(s): Timothy G. Gregoire, Harry T. Valentine, George M. Furnival Source: Ecology, Vol. 76, No. 4, (Jun., 1995), ppPubli Add to Reading List Source URL: www.planta.cn Language: English - Date: 2008-06-26 04:47:03 Monte Carlo methods Data analysis Computational statistics Importance sampling Sampling Estimator Uniform distribution Variance Standard error Statistics Statistical inference Estimation theory
92	Appendix 1. Vegetation/Soil sampling protocol Importance of each sampled component: Stem water is representative of the source water to which plants have access. This data allows for the assessment of the primary water p Add to Reading List Source URL: www-naweb.iaea.org Language: English - Date: 2005-05-13 09:03:51 Botany Medical equipment Plant morphology Blood Vacutainer Leaf Transpiration Soil test Soil Biology Plant physiology Hydrology
93	Truncation-free Stochastic Variational Inference for Bayesian Nonparametric Models Chong Wang⇤ Machine Learning Department Carnegie Mellon University [removed] Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:23 Dirichlet process Latent Dirichlet allocation Mixture model Bayesian inference Importance sampling Inference Conjugate prior Hierarchical Bayes model Dirichlet distribution Statistics Bayesian statistics Variational Bayesian methods
94	c:/tmp/suggest_vldb2008.dvi Add to Reading List Source URL: www.vldb.org Language: English - Date: 2008-10-22 09:22:51 Monte Carlo methods Probability theory Statistical theory M-estimator Estimator Importance sampling Rejection sampling Variance Bias of an estimator Statistics Estimation theory Statistical inference
95	Coresets for the DP-Means Clustering Problem Olivier Bachem ETH Zurich Mario Lu˘ci´c Add to Reading List Source URL: www.opt-ml.org Language: English - Date: 2014-12-10 11:28:14 Cluster analysis K-means++ K-means clustering Importance sampling Unsupervised learning Dirichlet process Data stream clustering Determining the number of clusters in a data set Statistics Computational statistics Machine learning
96	STATISTICS IN MEDICINE Statist. Med. 2009; 28:2202–2229 Published online in Wiley InterScience (www.interscience.wiley.com) DOI: [removed]sim[removed]Respondent-driven sampling as Markov chain Monte Carlo Add to Reading List Source URL: 5harad.com Language: English - Date: 2014-09-24 20:04:52 Signal processing Markov models Estimation theory Estimator Variance Bias of an estimator Importance sampling Markov chain Sampling Statistics Statistical inference Monte Carlo methods
97	INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING Int. J. Numer. Meth. Engng[removed]Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: [removed]nme.4779 A decomposition-based approach to unce Add to Reading List Source URL: web.mit.edu Language: English - Date: 2014-10-06 09:26:14 Cognition Measurement Statistical theory Uncertainty Importance sampling Function Discrete Fourier transform Sensitivity analysis Mathematics Statistics Mathematical analysis
98	Copyright  2003 JMASM, Inc. 1538 – [removed]/$30.00 Journal of Modern Applied Statistical Methods May 2003, Vol. 2, No 1, 27-49 Add to Reading List Source URL: www.datamineit.com Language: English - Date: 2008-06-09 08:40:00 Sampling Statistical inference Non-parametric statistics Resampling Importance sampling Statistical power Sample size determination Sample Permutation Statistics Statistical tests Monte Carlo methods
99	Microsoft Word - scai_hamalainen5.doc Add to Reading List Source URL: www.stes.fi Language: English - Date: 2006-10-20 01:29:00 Mathematics Statistics Evolutionary algorithms Variance reduction Cybernetics Genetic algorithm Monte Carlo integration Global optimization Importance sampling Monte Carlo methods Mathematical optimization Applied mathematics
100	1 Particle Filter Design using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments Eric A. Lehmann†,∗ and Robert C. Williamson‡ Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2005-05-29 23:20:55 Statistical inference Computational statistics Estimation theory Particle filter Importance sampling Kalman filter Bootstrapping Normal distribution Resampling Statistics Monte Carlo methods Robot control

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