'Bayesian inference' diaporamas de présentation

Lecture 16: Unsupervised Learning from Text

Lecture 16: Unsupervised Learning from Text. Padhraic Smyth Department of Computer Science University of California, Irvine . Outline. General aspects of text mining Named-entity extraction, question-answering systems, etc Unsupervised learning from text documents Motivation

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Learning Bayesian Networks from Data

Learning Bayesian Networks from Data. Nir Friedman Daphne Koller Hebrew U. Stanford . Overview. Introduction Parameter Estimation Model Selection Structure Discovery Incomplete Data Learning from Structured Data. Qualitative part :

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Bugs

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www.poulinhugin.com

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(Mis)understanding medical information: healthcare professionals and laymen alike

(Mis)understanding medical information: healthcare professionals and laymen alike. Talya Miron-Shatz, Ph.D. Center for Health and Wellbeing Princeton University Talk at the School of Public Affairs, Baruch College. Agenda. Who should understand medical information?

640 views • 46 slides

Ionatan J. Kuperwajs Howard Hughes Medical Institute Janelia Research Campus, Turaga Lab

Bayesian Inference of Neural Activity and Connectivity from All-Optical Interrogation of a Neural Circuit. Ionatan J. Kuperwajs Howard Hughes Medical Institute Janelia Research Campus, Turaga Lab. Talk Outline. Problem + Dataset, VI Model Framework Recognition + Generative Models

475 views • 46 slides

Intelligent data analysis B iomarker discovery II.

Intelligent data analysis B iomarker discovery II. Peter Antal antal@mit.bme.hu. Overview. Biomarkers The Bayesian statistical approach Partial multivariate analysis Marginalization, sub-, sup-relevance Frontlines Causal , confounded extension Multitarget (multidimensional)extension

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Introduction to Spatial Regression

640 views • 40 slides

Bayesian Nonparametric Classification and Applications

Department of Electrical and Computer Engineering. Zhu Han Department of Electrical and Computer Engineering University of Houston. Thanks to Nam Nguyen , Guanbo Zheng , and Dr. Rong Zheng. Bayesian Nonparametric Classification and Applications. Bayesian Nonparametric Classification.

510 views • 40 slides

Rating Table Tennis Players

Rating Table Tennis Players. An application of Bayesian inference. Ratings. The USATT rates all members A rating is an integer between 0 and 3000. Fan Yi Yong 2774. Example. Lee Bahlman 2045 Dell Sweeris 2080. Todd Sweeris. Old System. Example. Lee Bahlman (2045)

755 views • 31 slides

Multi-Model Data Fusion for Hydrological Forecasting

Multi-Model Data Fusion for Hydrological Forecasting. Linda See 1 and Bob Abrahart 2 1 Centre for Computational Geography, University of Leeds, UK 2 School of Earth and Environmental Sciences, University of Greenwich, UK. What is Data Fusion?.

316 views • 23 slides

Nonparametric hidden Markov models

Nonparametric hidden Markov models. Jurgen Van Gael and Zoubin Ghahramani. Introduction. HM models: time series with discrete hidden states Infinite HM models ( iHMM ): nonparametric Bayesian approach Equivalence between Polya urn and HDP interpretations for iHMM

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CMSC 471 Fall 2009

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Fusing Multiple Video Sensors for Surveillance

Fusing Multiple Video Sensors for Surveillance. By: Lauro Snidaro Ingrid Visentini Gian Luca Foresti. Presented By: Sushma Ajjampur Jagadeesh. Introduction. Why surveillance system? What is video surveillance system? Fundamental issues with surveillance system.

372 views • 18 slides

Swets et al (1961)

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Connectionism

382 views • 14 slides

Sample variance and sample error

Y=sqrt(X). sqrt(S 2 ). ?. ? 2. S 2. Sample variance and sample error. We learned recently how to determine the sample variance using the sample mean. How do we translate this to an unbiased estimate of the error on a single point?

789 views • 12 slides

More about Posterior Distributions

More about Posterior Distributions. The process of Bayesian inference involves passing from a prior distribution to a posterior distribution. It is natural to expect that some general relations might hold between these two distributions.

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Bayesian Inference using Gibbs Sampling (BUGS) version 0.5 Manual

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