'Bayesian' presentation slideshows

Advanced Topics in Computer Systems: Machine Learning and Data Mining Systems Winter 2007

2.09k views • 189 slides

Friday, 15 February 2008 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org

Lecture 11 of 42. Artificial Neural Networks: Backprop (continued) and Intro to SVM. Friday, 15 February 2008 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org/Courses/Spring-2008/CIS732/ Readings:

787 views • 64 slides

Arctic SST retrieval in the CCI project

Arctic SST retrieval in the CCI project. Owen Embury Chris Merchant University of Reading. SST CCI Phase 1. Combine ATSR accuracy with AVHRR coverage Optimal Estimation (OE) retrieval Cross referenced to ARC SST Diurnal variability adjustment Report SSTs at standard depth and time of day

765 views • 62 slides

Statistical methods in LHC data analysis part II.2

Statistical methods in LHC data analysis part II.2. Luca Lista INFN Napoli. Contents. Upper limits Treatment of background Bayesian limits Modified frequentist approach (CL s method) Profile likelihood Nuisance parameters and Cousins-Highland approach.

707 views • 57 slides

Coalition-Based Cooperative Packet Delivery under Uncertainty: A Dynamic Bayesian Coalitional Game

Coalition-Based Cooperative Packet Delivery under Uncertainty: A Dynamic Bayesian Coalitional Game. Authors: Khajonpong Akkarajitsakul , Ekram Hossain , and Dusit Niyato. Paper Presentation By : Gaurav Dixit (gdixit@vt.edu). GAME THEORY. Prisoner's Dilemma. TCP collision handling.

689 views • 48 slides

614 views • 47 slides

Statistical Methods for Data Analysis parameter estimate

Statistical Methods for Data Analysis parameter estimate. Luca Lista INFN Napoli. Contents. Parameter estimates Likelihood function Maximum Likelihood method Problems with asymmetric errors. Meaning of parameter estimate. We are interested in some physical unknown parameters

710 views • 42 slides

Remote RNA homology detection

Remote RNA homology detection. Sean R. Eddy HHMI Janelia Farm Research Campus. Probability theory is nothing but common sense reduced to calculation. Laplace (1819). RNAs conserve both secondary structure and sequence. There are many RNA structures of interest.

488 views • 36 slides

Truth Finding on the Deep WEB: Is the Problem Solved

501 views • 35 slides

Bayesian and Least Squares fitting:

474 views • 29 slides

Comparing Classical and Bayesian Approaches to Hypothesis Testing

Comparing Classical and Bayesian Approaches to Hypothesis Testing. James O. Berger Institute of Statistics and Decision Sciences Duke University www.stat.duke.edu. Outline. The apparent overuse of hypothesis testing When is point null testing needed? The misleading nature of P-values

719 views • 27 slides

J. Daunizeau Brain and Spine Institute, Paris, France

J. Daunizeau Brain and Spine Institute, Paris, France Wellcome Trust Centre for Neuroimaging, London, UK. Bayesian inference. Overview of the talk. 1 Probabilistic modelling and representation of uncertainty 1.1 Bayesian paradigm 1.2 Hierarchical models

357 views • 26 slides

The Reverend Bayes and Solar Neutrinos

311 views • 23 slides

Practical Design for Discrete Choice Experiments

Practical Design for Discrete Choice Experiments. Bradley Jones, SAS Institute August 13, 2008. Discrete Choice Experiment Setup. Respondents indicate the alternative they prefer most in each choice set A lternatives are called profiles Each profile is a combination of attribute levels

342 views • 19 slides

Module C3

248 views • 14 slides

A Unifying Framework for Acoustic Localization

A Unifying Framework for Acoustic Localization. Stanley T. Birchfield Dept. of Electrical and Computer Engineering Clemson University Clemson, South Carolina USA. Acoustic Localization. distributed. compact. Problem: Use microphone signals to determine sound source location.

308 views • 13 slides

Statistics Forum Follow-up info for Physics Coordination 24 June, 2011

Statistics Forum Follow-up info for Physics Coordination 24 June, 2011. Glen Cowan, Eilam Gross. Main questions. What do we see as the main way forward with CMS? What do we recommend in the short term (summer 2011)? What do we recommend after summer 2011?. The way forward with CMS.

207 views • 13 slides

Innovation: the next step

229 views • 10 slides

CH751 ????? ?? Uncertainties in Intelligent Systems

CH751 ????? ?? Uncertainties in Intelligent Systems. 2004 ?? ? 1 ??. ??? ??. ?? ?? ??? ( ?? C515; ? 2123-2720; sbcho@cs.yonsei.ac.kr) ? ??? : http://sclab.yonsei.ac.kr/Courses/04FuSys ?? ?? ? 6, 7, ? 7 (C520) ?? ?? ? 8, 9, ? 9 ?? ?? ???.

127 views • 7 slides

Bayesian Methods and Computing in Medicine and Health Policy Oct. 12-14, 2000 R. David Thomas

Schedule Day 1: Thursday Lunch - Noon Intro to the Bayesian Approach Probability Prior, likelihood, posterior, predictive distributions GUSTO: A Case Study Elicitation Decision Analysis Design Dinner Intro to Software - First Bayes, Splus, BUGS Day 2: Friday Breakfast

118 views • 2 slides