'Bayesian inference' presentation slideshows

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Rating Table Tennis Players

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)

By richard_edik
(305 views)

Learning Bayesian Networks from Data

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 :

By taran
(205 views)

Bugs

Bugs

Bugs. Strategy. Remember, these models are inherently complex You don’t want to make them needlessly complex Start with lm/glm Estimate in lmer Move to Bugs You can do the first two. Bayesian Inference and priors. The difficulty is in estimating the two levels of model simultaneously.

By lilith
(192 views)

More about Posterior Distributions

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.

By nam
(120 views)

www.poulinhugin.com

www.poulinhugin.com

www.poulinhugin.com. Overview. Brief Project History Hugin Expert A/S and Bayesian Technology Discussion Poulin Automation Tool Discussion. Hugin Software?. Product maturity and optimisation produce the world ’ s fastest Bayesian inference engine

By jaegar
(84 views)

Sample variance and sample error

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?

By nishi
(271 views)

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

(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?

By holland
(156 views)

CMSC 471 Fall 2009

CMSC 471 Fall 2009

CMSC 471 Fall 2009. Class #18 – Thursday, October 29. Today’s class. Probability theory Bayesian inference From the joint distribution Using independence/factoring From sources of evidence. Bayesian Reasoning. Chapter 13. Sources of uncertainty. Uncertain inputs Missing data

By titus
(118 views)

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

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

By terrel
(2 views)

Connectionism

Connectionism

Connectionism. “Frank Rosenblatt, Alan M. Turing, Connectionism, and AI” May 6, 2011 Version 4.0; 05/06/2011 John M. Casarella Proceedings of Student/Faculty Research Day Ivan G. Seidenberg School of CSIS, Pace University. Abstract.

By lavey
(212 views)

Introduction to Spatial Regression

Introduction to Spatial Regression

Introduction to Spatial Regression. Glen Johnson, PhD Lehman College / CUNY School of Public Health glen.johnson@lehman.cuny.edu. Typical scenario: Have a health outcome and covariables aggregated at a common geographic level, such as counties, census tracts, ZIP codes …

By beulah
(155 views)

Lecture 16: Unsupervised Learning from Text

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

By mulan
(130 views)

Multi-Model Data Fusion for Hydrological Forecasting

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?.

By mari
(75 views)

Today’s class

Today’s class

Today’s class. Probability theory Bayesian inference From the joint distribution Using independence/factoring From sources of evidence. Bayesian Reasoning. Chapter 13. Sources of uncertainty. Uncertain inputs Missing data Noisy data Uncertain knowledge

By monifa
(71 views)

Nonparametric hidden Markov models

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

By adolph
(167 views)

Intelligent data analysis B iomarker discovery II.

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

By hoai
(132 views)

Bayesian Nonparametric Classification and Applications

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.

By rio
(99 views)

Bayesian Inference using Gibbs Sampling (BUGS) version 0.5 Manual

Bayesian Inference using Gibbs Sampling (BUGS) version 0.5 Manual

Tomas Radivoyevitch · David G. Hoel . Biologically-based risk estimation for radiation-induced chronic myeloid leukemia. Radiat Environ Biophys (2000) 39:153–159 Suppose we have vectors of model parameters θ and observed data X . Bayes theorem

By quilla
(72 views)

Swets et al (1961)

Swets et al (1961)

Swets et al (1961). Key ideas. continuity in stimulus-induced mental states variability in these states sensitivity (d’) role of prior probability and payoffs bias, criterion… Bayesian inference n ormative/optimal model, ideal observer. Your questions.

By quasim
(125 views)

Fusing Multiple Video Sensors for Surveillance

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.

By yamka
(175 views)

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