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Fisher information
Results: 521

#	Item
311	Tree-reweighted belief propagation algorithms and approximate ML estimation by pseudo-moment matching Martin J. Wainwright Dept. of EECS, UC Berkeley Berkeley, CA 94705 Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2002-11-19 01:50:14 M-estimators Maximum likelihood Statistical theory Entailment Fisher information Belief propagation Statistics Estimation theory Logic
312	Communication-Efficient Algorithms for Statistical Optimization 1 Yuchen Zhang1 Add to Reading List Source URL: www.cs.berkeley.edu Language: English - Date: 2012-11-14 05:54:22 M-estimator Parametric model Likelihood function Score Bootstrapping Stochastic approximation Loss function Big O notation Maximum likelihood Statistics Estimation theory Fisher information
313	References Alder, M. A., & Fisher, C. W[removed]Center for the improvement of early reading achievement: Early reading programs in high-poverty schools: A case study of beating the odds. The Reading Teacher, 54(6), 616- Add to Reading List Source URL: center.serve.org Language: English - Date: 2013-07-17 12:56:46 Educational technology Information and communication technologies in education Information technology Direct Instruction Achievement gap in the United States Marcia A. Invernizzi Reading Recovery Education Year of birth missing Communication
314	Journal of Machine Learning Research[removed]3363 Submitted 3/13; Published[removed]Communication-Efficient Algorithms for Statistical Optimization Yuchen Zhang Add to Reading List Source URL: jmlr.org Language: English - Date: 2013-12-09 15:15:53 Fisher information Bias of an estimator M-estimator Loss function Maximum likelihood Fisher consistency Statistics Estimation theory Statistical theory
315	Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data∗ Ashok N. Srivastava, Ph.D.† September 15, 2003 This paper was submitted to the 2004 SIAM Data Mining Conference on September 15, 2003. Add to Reading List Source URL: www.riacs.edu Language: English - Date: 2006-05-22 00:23:38 Cluster analysis Fisher kernel Information retrieval Gaussian function Mixture model Kernel Dirac delta function Expectation–maximization algorithm Support vector machine Statistics Non-parametric statistics Statistical classification
316	Kennedy Krieger Institute Wish List All donations can be delivered to Jaime Ball, Director of Guest Relations & Volunteer Services, at 707 North Broadway, Baltimore, MD[removed]For more information and to make deliver ar Add to Reading List Source URL: www.kennedykrieger.org Language: English - Date: 2013-12-31 08:49:11 Entertainment Recreation Fisher-Price Toy Educational toys Lego Doll Super Mario My Little Pony Play Mattel Childhood
317	Understanding predictive information criteria for Bayesian models∗ Andrew Gelman†, Jessica Hwang‡, and Aki Vehtari§ 14 Aug 2013 Abstract We review the Akaike, deviance, and Watanabe-Akaike information criteria fro Add to Reading List Source URL: www.stat.columbia.edu Language: English - Date: 2013-08-14 15:26:41 Machine learning Bayesian statistics Regression analysis Deviance information criterion Maximum likelihood Cross-validation Fisher information Akaike information criterion Bayesian information criterion Statistics Estimation theory Model selection
318	On the number of support points of maximin and Bayesian D-optimal designs in nonlinear regression models Dietrich Braess Holger Dette Add to Reading List Source URL: homepage.rub.de Language: English - Date: 2004-12-20 12:06:43 Estimation theory Design of experiments Statistical methods Linear regression Optimal design Fisher information Logistic regression Statistics Regression analysis Econometrics
319	CALIFORNIA PRIVACY REQUEST FORM California residents have the right to request information from Mattel, Fisher-Price and American Girl on sharing of personal information with third parties for their marketing purposes on Add to Reading List Source URL: corporate.mattel.com Language: English - Date: 2012-12-17 16:48:51 Law Privacy Mattel Internet privacy El Segundo California Fax Personally identifiable information Ethics Technology Identity management
320	B Analysis Methods Phil Marshall, Licia Verde, Hu Zhan This chapter describes the statistical analysis methods that are used in previous chapters either to forecast LSST performance or as suggested to analyze LSST data. Add to Reading List Source URL: www.lsst.org Language: English - Date: 2010-07-15 13:50:07 Statistical theory Bayesian inference Statistical inference Maximum likelihood Fisher information Bayes factor Likelihood function Normal distribution Confidence interval Statistics Estimation theory Bayesian statistics

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