'Correlated variables' diaporamas de présentation

Introduction to Generalized Linear Models

Introduction to Generalized Linear Models. Prepared by Louise Francis Francis Analytics and Actuarial Data Mining, Inc. October 3, 2004. Objectives. Gentle introduction to Linear Models and Generalized Linear Models Illustrate some simple applications

673 views • 65 slides

Mutidimensional Data Analysis

Mutidimensional Data Analysis. Growth of big databases requires important data processing. ? Need for having methods allowing to extract this information from large data tables. Tree categories of Data Analysis methods : Description : to describe a phenomenon without prejudice

579 views • 48 slides

Computational Architectures in Biological Vision, USC

353 views • 35 slides

Computational Architectures in Biological Vision, USC, Spring 2001

Computational Architectures in Biological Vision, USC, Spring 2001. Lecture 7. Coding and Representation Reading Assignments: None. Variability in neuronal responses. Neuronal response f(.) can be represented as the sum of:

357 views • 35 slides

Principal Component Analysis

Principal Component Analysis. Philosophy of PCA. Introduced by Pearson (1901) and Hotelling (1933) to describe the variation in a set of multivariate data in terms of a set of uncorrelated variables

501 views • 34 slides

Radiative and EW Penguin B-Decays with the Belle Detector

Radiative and EW Penguin B-Decays with the Belle Detector. Tom Ziegler ? Beauty 2003 ? Pittsburgh, Pennsylvania 13-18 th Oct 2003. Introduction Rare/Radiative Decays Purely Leptonic Decays Status & Perspective. Introduction and Motivation.

452 views • 32 slides

Statistical Methods in Computer Science

Data 3: Correlations and Dependencies Ido Dagan. Statistical Methods in Computer Science. Connecting Variables. So far: talked about the data reflected by a single variable Common scientific goal: relate between variables Find out whether a relation exists between values of variables

310 views • 23 slides

Practical Statistics for Physicists

Louis Lyons Imperial College and Oxford CMS expt at LHC l.lyons@physics.ox.ac.uk. Practical Statistics for Physicists. CERN Latin American School March 2015. Extra Lecture: Learning to love the Error Matrix. Introduction via 2-D Gaussian Understanding covariance

225 views • 22 slides

Markov-Chain Monte Carlo

Markov-Chain Monte Carlo. Instead of integrating, sample from the posterior. The histogram of chain values for a parameter is a visual representation of the (marginalized) probability distribution for that parameter. Can then easily compute confidence intervals:

296 views • 11 slides

Markov-Chain Monte Carlo

Markov-Chain Monte Carlo. Instead of integrating, sample from the posterior. The histogram of chain values for a parameter is a visual representation of the (marginalized) probability distribution for that parameter. Can then easily compute confidence intervals:

161 views • 11 slides