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Probability theory
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#	Item
571	STAT111 Homework 4 Solution Total: 28 pointspoints, 1 each) µ = 190, σ = 288 ¯ = ¯ = 190, SD(X) Add to Reading List Source URL: www-stat.wharton.upenn.edu Language: English - Date: 2016-03-28 11:37:19 Probability distributions Linear algebra Norm Probability theory
572	Nonparametric spatial models for clustered ordered periodontal data Dipankar Bandyopadhyay∗ Department of Biostatistics Virginia Commonwealth University, Richmond, VA, USA Add to Reading List Source URL: www.carloalberto.org Language: English - Date: 2016-04-19 07:03:23 Statistics Regression analysis Probability distributions Estimation theory Stable distributions Normal distribution Probit model Linear regression Nonparametric statistics Multivariate normal distribution Gibbs sampling Probit
573	Cognition–300 Contents lists available at SciVerse ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT Add to Reading List Source URL: cocosci.berkeley.edu Language: English - Date: 2015-10-08 17:16:09 Statistical inference Hypothesis testing Design of experiments Statistical theory Philosophy of science Statistical hypothesis testing Bayesian inference Statistics Sampling Probability distribution Experiment Monte Carlo method
574	mTunes: Efficient Post-Silicon Tuning of Mixed-Signal/RF Integrated Circuits Based on Markov Decision Process Manzil Zaheer and Fa Wang Chenjie Gu Add to Reading List Source URL: manzil.ml Language: English - Date: 2016-01-03 02:26:38 Markov models Markov processes Probability theory Applied mathematics Statistics Receiver Dynamic programming Markov decision process Stochastic control Tuner Artificial neural network Tuning
575	Uncertain<T>: A First-Order Type for Uncertain Data Add to Reading List Source URL: www.cs.utexas.edu Language: English - Date: 2014-01-10 00:11:41 Statistics Statistical theory Statistical inference Bayesian statistics Statistical models Measurement Bayesian network Probability distribution Bayesian inference Normal distribution Global Positioning System Confidence interval
576	Vida: How to use Bayesian inference to de-anonymize persistent communications George Danezis1 and Carmela Troncoso2 1 2 Add to Reading List Source URL: www0.cs.ucl.ac.uk Language: English - Date: 2013-10-10 06:48:53 Statistics Statistical theory Probability Probability distributions Statistical inference IP Bayesian inference Gibbs sampling Markov chain Binomial distribution Bayesian network
577	CS 493: Algorithms for Massive Data Sets Feb 21, Local Models, Bloom Filter Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2002-03-15 13:15:13 Hashing Search algorithms Statistical theory Bloom filter Bit array Probability distributions Hash function Entropy Golomb coding KullbackLeibler divergence Birthday problem
578	Dynamic Sensing: Better Classification under Acquisition Constraints Add to Reading List Source URL: jmlr.org Language: English - Date: 2015-09-16 19:38:47 Statistics Mathematical analysis Probability Probability distributions Generalization error Normal distribution Expected value Spectral theory of ordinary differential equations Beta distribution
579	A Scaling Result for Explosive Processes M. Mitzenmacher J. Spencer Add to Reading List Source URL: www.eecs.harvard.edu Language: English - Date: 2002-07-08 11:27:45 Probability distributions Normal distribution Cumulative distribution function Statistical theory Stochastic processes Information theory Binomial distribution
580	On the Relationship between Sum-Product Networks and Bayesian Networks Han Zhao HAN . ZHAO @ UWATERLOO . CA Mazen Melibari MMELIBAR @ UWATERLOO . CA Add to Reading List Source URL: jmlr.org Language: English - Date: 2015-09-16 19:38:50 Graphical models Statistical models Probability theory Bayesian network Networks Variable elimination Markov random field

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