'Sufficient statistics' presentation slideshows

Partially Observable Markov Decision Processes

Partially Observable Markov Decision Processes. Lee Wee Sun School of Computing leews@comp.nus.edu.sg. Dialog Systems. Aim : Find out what the person wants Actions : Ask appropriate questions Observations : Output of speech recognizer. Video from

1.55k views • 125 slides

Adaptive Randomized Trial Designs

Adaptive Randomized Trial Designs. Michael Rosenblum Department of Biostatistics Johns Hopkins Bloomberg School of Public Health April 9-May 3, 2010 Course Notes and Readings at: http://people.csail.mit.edu/mrosenblum/Teaching/adaptive_designs_2010.html.

1.14k views • 91 slides

Yucheng Low

A Framework for Machine Learning and Data Mining in the Cloud . Yucheng Low. Joseph Gonzalez. Aapo Kyrola. Danny Bickson. Carlos Guestrin. Joe Hellerstein. Big Data is Everywhere. 900 Million Facebook Users. 28 Million Wikipedia Pages. 6 Billion Flickr Photos.

663 views • 54 slides

Neutron Transversity with BigBite + Super BigBite C12-09-018

Neutron Transversity with BigBite + Super BigBite C12-09-018. Andrew Puckett, LANL Hall A Collaboration Meeting 6/10/11. Outline. Physics Motivation Experiment Layout BigBite as e arm SBS as h arm High Luminosity 3 He target Monte Carlo Simulation Kinematic Coverage Azimuthal Coverage

666 views • 54 slides

Inference About a Mean Vector, Part 1

583 views • 36 slides

Using Simulation: Why the Planning is as Important as the Execution

Using Simulation: Why the Planning is as Important as the Execution. Andy Grieve, Ph.D. SVP Clinical Trials Methodology Innovation Centre Andy.Grieve@aptivsolutions.com. Outline. History of Simulation Introduction Basic Questions in Planning Ex. Sample Sizing Simulations

510 views • 32 slides

Computational models for imaging analyses

Computational models for imaging analyses. Zurich SPM Course February 14, 2014 Christoph Mathys. What the brain is about. What do our imaging methods measure? Brain activity. But when does the brain become active? When predictions have to be adjusted.

456 views • 31 slides

The Physics of HFT the Heavy Flavor Tracker at STAR Spiros Margetis

The Physics of HFT the Heavy Flavor Tracker at STAR Spiros Margetis Kent State University, USA. Excited QCD 2010 . The Bottom Line.

421 views • 28 slides

A Gentle Introduction to the EM Algorithm

A Gentle Introduction to the EM Algorithm. Ted Pedersen Department of Computer Science University of Minnesota Duluth tpederse@d.umn.edu. A unifying methodology. Dempster, Laird & Rubin (1977) unified many strands of apparently unrelated work under the banner of The EM Algorithm

449 views • 24 slides

Using the Fisher kernel method to detect remote protein homologies

342 views • 24 slides

312 views • 23 slides

A gentle introduction to Gaussian distribution

A gentle introduction to Gaussian distribution. X = 0. X = 1. X: Random variable. Review. Random variable Coin flip experiment. P(x). P(x) >= 0. 0. 1. x. Review. Probability mass function (discrete). Any other constraints? Hint: What is the sum?. Example: Coin flip experiment.

386 views • 21 slides

A gentle introduction to Gaussian distribution

A gentle introduction to Gaussian distribution. X = 0. X = 1. X: Random variable. Review. Random variable Coin flip experiment. P(x). P(x) >= 0. 0. 1. x. Review. Probability mass function (discrete). Any other constraints? Hint: What is the sum?. Example: Coin flip experiment.

562 views • 21 slides

Rotation Tests - Computing exact adjusted p-values in multiresponse experiments

370 views • 21 slides

Controversy? What controversy? An attempt to structure the debate on adaptive designs

Controversy? What controversy? An attempt to structure the debate on adaptive designs. Marc Vandemeulebroecke MCP, Vienna, 11.7.2007. Vandemeulebroecke Controversy? What controversy? 1. Examples (1): Sequential analysis.

567 views • 20 slides

LECTURE 07: MAXIMUM LIKELIHOOD AND BAYESIAN ESTIMATION

428 views • 20 slides

Adaptation Techniques in Automatic Speech Recognition

382 views • 17 slides

DisCo : Distributed Co-clustering with Map-Reduce

DisCo : Distributed Co-clustering with Map-Reduce. Spiros Papadimitriou Jimeng Sun IBM T.J. Watson Research Center Hawthorne, NY, USA Reporter: Nai-Hui , Ku. Outline. Introduction Related Work Distributed Mining Process Co-clustering Huge Datasets Experiments Conclusions.

413 views • 17 slides

Bayesian Nonparametric Matrix Factorization for Recorded Music

Bayesian Nonparametric Matrix Factorization for Recorded Music. Matthew D. Hoffman, David M. Blei, Perry R. Cook. Presented by Lu Ren Electrical and Computer Engineering Duke University. Outline. Introduction . GaP-NMF Model Variational Inference Evaluation Related Work Conclusions.

238 views • 16 slides

A K-Means Based Bayesian Classifier Inside a DBMS Using SQL & UDFs

A K-Means Based Bayesian Classifier Inside a DBMS Using SQL & UDFs. Ph.D Showcase, Dept. of Computer Science Sasi Kumar Pitchaimalai Ph.D Candidate Database Systems Group, Department of Computer Science University of Houston Advisor: Dr. Carlos Ordonez. Motivation.

241 views • 11 slides