Probability distributions

Results: 694



#Item
11A Result of Vapnik with Applications Martin Anthony Department of Statistical and Mathematical Sciences London School of Economics Houghton Street London WC2A 2AE, U.K.

A Result of Vapnik with Applications Martin Anthony Department of Statistical and Mathematical Sciences London School of Economics Houghton Street London WC2A 2AE, U.K.

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Source URL: www.maths.lse.ac.uk

Language: English - Date: 2000-04-03 14:53:14
12Optimizing TTL Caches under Heavy-Tailed Demands Andrés Ferragut Ismael Rodríguez  Fernando Paganini

Optimizing TTL Caches under Heavy-Tailed Demands Andrés Ferragut Ismael Rodríguez Fernando Paganini

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Source URL: fi.ort.edu.uy

Language: English
13Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row

Learning stick-figure models using nonparametric Bayesian priors over trees Edward W. Meeds, David A. Ross, Richard S. Zemel, and Sam T. Roweis Department of Computer Science University of Toronto {ewm, dross, zemel, row

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Source URL: www.cs.toronto.edu

Language: English - Date: 2008-08-08 22:07:31
14GSDLAB TECHNICAL REPORT  Why CART Works for Variability-Aware Performance Prediction? An Empirical Study on Performance Distributions

GSDLAB TECHNICAL REPORT Why CART Works for Variability-Aware Performance Prediction? An Empirical Study on Performance Distributions

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Source URL: www.infosun.fim.uni-passau.de

Language: English - Date: 2013-05-15 08:35:26
15P1: JSN/VSK  P2: JSN International Journal of Computer Vision

P1: JSN/VSK P2: JSN International Journal of Computer Vision

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Source URL: www.stat.ucla.edu

Language: English - Date: 2001-02-25 14:00:10
16JMLR: Workshop and Conference Proceedings vol 40:1–18, 2015  Learning the dependence structure of rare events: a non-asymptotic study Nicolas Goix Anne Sabourin

JMLR: Workshop and Conference Proceedings vol 40:1–18, 2015 Learning the dependence structure of rare events: a non-asymptotic study Nicolas Goix Anne Sabourin

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Source URL: jmlr.org

Language: English - Date: 2015-07-20 20:08:36
17Exercises 3: unconstrained maximization Let’s investigate Fact 1 from Daniel Wilhelm’s lecture notes. Fact 1: 1. if f has a local max (min) at point x∗ , then Df (x∗ ) = 0 and D2 f (x∗ ) is negative (positive)

Exercises 3: unconstrained maximization Let’s investigate Fact 1 from Daniel Wilhelm’s lecture notes. Fact 1: 1. if f has a local max (min) at point x∗ , then Df (x∗ ) = 0 and D2 f (x∗ ) is negative (positive)

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Source URL: dl.dropboxusercontent.com

Language: English
18Chapter 6  Parameter Estimation Take a random variable x described by a pdf f (x): the sample space is defined to be the set of all possible values of x. The set of n independent measurements of the random variable x, {x

Chapter 6 Parameter Estimation Take a random variable x described by a pdf f (x): the sample space is defined to be the set of all possible values of x. The set of n independent measurements of the random variable x, {x

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Source URL: ihp-lx.ethz.ch

Language: English - Date: 2015-03-31 05:26:54
19Ecology, 86(5), 2005, pp. 1124–1134 q 2005 by the Ecological Society of America STATISTICS OF EXTREMES: MODELING ECOLOGICAL DISTURBANCES RICHARD W. KATZ,1,3 GRACE S. BRUSH,2

Ecology, 86(5), 2005, pp. 1124–1134 q 2005 by the Ecological Society of America STATISTICS OF EXTREMES: MODELING ECOLOGICAL DISTURBANCES RICHARD W. KATZ,1,3 GRACE S. BRUSH,2

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Source URL: www.isse.ucar.edu

Language: English - Date: 2013-06-24 15:39:57
20James–Stein type estimators of variances

James–Stein type estimators of variances

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Source URL: www.pstat.ucsb.edu

Language: English - Date: 2012-09-18 20:28:37