Probability distribution

Results: 3066



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61Microsoft PowerPoint - Chris Harris CFC 07.ppt

Microsoft PowerPoint - Chris Harris CFC 07.ppt

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Source URL: www.bbk.ac.uk

Language: English - Date: 2007-03-27 13:46:59
62Data-driven Jump Detection Thresholds for Application in Jump Regressions∗ Robert Davies† and George Tauchen‡  September 17, 2015

Data-driven Jump Detection Thresholds for Application in Jump Regressions∗ Robert Davies† and George Tauchen‡ September 17, 2015

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Source URL: www.cb.cityu.edu.hk

Language: English - Date: 2016-07-08 00:06:04
63HP Prime APSI 2015 Webinar: Programming in HP PPL  Version 1.2 Programming in HP PPL

HP Prime APSI 2015 Webinar: Programming in HP PPL Version 1.2 Programming in HP PPL

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Source URL: www.hp-prime.de

Language: English - Date: 2016-02-23 03:05:52
64Supplement for: A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky1,2,3 , Florian Franzen1,2,3 , Giacomo Bassetto1,3 , Jakob H. Macke1,3 1 Max Planck Institute f

Supplement for: A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky1,2,3 , Florian Franzen1,2,3 , Giacomo Bassetto1,3 , Jakob H. Macke1,3 1 Max Planck Institute f

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Source URL: www.mackelab.org

Language: English - Date: 2016-08-04 15:02:45
65CS264: Beyond Worst-Case Analysis Lecture #14: Smoothed Analysis of Pareto Curves∗ Tim Roughgarden† November 5,

CS264: Beyond Worst-Case Analysis Lecture #14: Smoothed Analysis of Pareto Curves∗ Tim Roughgarden† November 5,

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Source URL: theory.stanford.edu

Language: English - Date: 2014-11-29 15:44:35
66STATIONARY TANGENT: THE DISCRETE AND NON-SMOOTH CASE U. KEICH Abstract. In [5] we define a stationary tangent process, or a locally optimal stationary approximation, to a real non-stationary smooth Gaussian process. Thi

STATIONARY TANGENT: THE DISCRETE AND NON-SMOOTH CASE U. KEICH Abstract. In [5] we define a stationary tangent process, or a locally optimal stationary approximation, to a real non-stationary smooth Gaussian process. Thi

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Source URL: www.maths.usyd.edu.au

Language: English - Date: 2002-12-09 16:28:18
67Exploiting Temporal Coherence in Forest Dynamics Simulation ∗ Pankaj K. Agarwal

Exploiting Temporal Coherence in Forest Dynamics Simulation ∗ Pankaj K. Agarwal

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Source URL: users.cs.duke.edu

Language: English - Date: 2015-07-24 06:57:53
68Journal of Machine Learning Research1094  Submitted 12/06; Revised 5/08; Published 5/09 An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak

Journal of Machine Learning Research1094 Submitted 12/06; Revised 5/08; Published 5/09 An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak

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Source URL: www.jmlr.org

Language: English - Date: 2009-05-03 20:39:39
69Lab Exercise: Samples of a covariance matrix GEOS 627: Inverse Problems and Parameter Estimation, Carl Tape Last compiled: February 11, 2015 Problem See class notes tarantola.pdf for background.

Lab Exercise: Samples of a covariance matrix GEOS 627: Inverse Problems and Parameter Estimation, Carl Tape Last compiled: February 11, 2015 Problem See class notes tarantola.pdf for background.

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Source URL: www.giseis.alaska.edu

Language: English - Date: 2015-02-11 20:03:06
70Asynchronous Knowledge Gradient Policy for Ranking and Selection

Asynchronous Knowledge Gradient Policy for Ranking and Selection

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Source URL: informs-sim.org

Language: English - Date: 2015-02-05 09:24:44