'Posterior distribution' diaporamas de présentation

Todd D. Little University of Kansas Director, Quantitative Training Program

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Scaling of the Cognitive Data and Use of Student Performance Estimates

Scaling of the Cognitive Data and Use of Student Performance Estimates. Guide to the PISA Data Analysis Manual. Classical Test Theory versus Item Response Theory. Questions What is the Rasch model? Why do International S urveys use the Rasch model or another IRT model?

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Nonparametric Link Prediction in Dynamic Graphs

Nonparametric Link Prediction in Dynamic Graphs. Purnamrita Sarkar (UC Berkeley) Deepayan Chakrabarti (Facebook) Michael Jordan (UC Berkeley). Link Prediction. Who is most likely to be interact with a given node?. Should Facebook suggest Alice as a friend for Bob ?. Alice. Bob.

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Learning with Bayesian Networks

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Using Covariates in Experiments: Design and Analysis

Using Covariates in Experiments: Design and Analysis. STA 320 Design and Analysis of Causal Studies Dr. Kari Lock Morgan and Dr. Fan Li Department of Statistical Science Duke University. Covariates. Pre-treatment variables X: n x k covariate matrix GOAL: balance between treatment groups.

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MDL

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Tutorial on Particle filters

Tutorial on Particle filters. Keith Copsey Pattern and Information Processing Group DERA Malvern. K.Copsey@signal.dera.gov.uk. Outline. Introduction to particle filters Recursive Bayesian estimation Bayesian Importance sampling Sequential Importance sampling (SIS)

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Efficient Learning in High Dimensions with Trees and Mixtures

Efficient Learning in High Dimensions with Trees and Mixtures. Marina Meila Carnegie Mellon University. Learning. Multidimensional data. Multidimensional (noisy) data Learning tasks - intelligent data analysis categorization (clustering) classification novelty detection

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Bayesian Analysis of Spatio -Temporal Dynamic Panel Models with Fixed and Random Effects

Bayesian Analysis of Spatio -Temporal Dynamic Panel Models with Fixed and Random Effects Mohammadzadeh, M . and Karami , H. Tarbiat Modares University, Tehran, Iran Rasouli , H. Trauma Research Center, Baqiyatallah University of Medical Sciences, Tehran Iran . Bayes2014

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Stochastic approximate inference

Stochastic approximate inference. Kay H. Brodersen Computational Neuroeconomics Group Department of Economics University of Zurich Machine Learning and Pattern Recognition Group Department of Computer Science ETH Zurich http://people.inf.ethz.ch/bkay/. When do we need approximate inference?.

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SAND2007-5696C

SAND2007-5696C.

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Non-Informative Priors Via Sieves and Packing Numbers

Non-Informative Priors Via Sieves and Packing Numbers. - S.Ghoshal - J.K.Ghosh - R.V.Ramamoorthi. (presented by Priyam Das).

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Reinforcement Learning

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High-resolution computational models of genome binding events

Yuan (Alan) Qi Joint work with Gifford and Young labs. High-resolution computational models of genome binding events. Dana-Farber Cancer Institute Jan 2007. ChIP-chip Experiments. ChIP-chip data: Encode valuable information about protein-DNA binding events. Goal:

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Perfect Sampling using MCMC and its application in Signal P rocessing

Perfect Sampling using MCMC and its application in Signal P rocessing. Presented by Sridivya Gajjarapu Florida State University sg12k@my.fsu.edu. Overview. Perfect Sampling Coupling from the Past technique Gibbs coupler Applications in signal processing. Perfect sampling.

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Efficient modelling of record linked data

Efficient modelling of record linked data. A missing data perspective Harvey Goldstein Record Linkage M ethodology Research Group Institute of Child Health University College London. Record Linkage.

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The Uniform Prior and the Laplace Correction

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Bayesian Inference using Gibbs Sampling (BUGS) version 0.5 Manual

152 views • 6 slides