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Matrix Factorization Methods Ahmet Oguz Akyuz

Matrix Factorization Methods Ahmet Oguz Akyuz

Matrix Factorization Methods Ahmet Oguz Akyuz Matrix Factorization Methods Principal component analysis Singular value decomposition Non-negative matrix factorization Independent component analysis Eigen decomposition Random projection Factor analysis Principal Component Anaylsis

By liam
(578 views)

Marginal Particle and Multirobot Slam: SLAM=‘SIMULTANEOUS LOCALIZATION AND MAPPING’

Marginal Particle and Multirobot Slam: SLAM=‘SIMULTANEOUS LOCALIZATION AND MAPPING’

Marginal Particle and Multirobot Slam: SLAM=‘SIMULTANEOUS LOCALIZATION AND MAPPING’ By Marc Sobel (Includes references to Brian Clipp Comp 790-072 Robotics) The SLAM Problem Given Robot controls Nearby measurements Estimate Robot state (position, orientation) Map of world features

By albert
(320 views)

Time-Varying Effects of Oil Supply Shocks on the US Economy

Time-Varying Effects of Oil Supply Shocks on the US Economy

Time-Varying Effects of Oil Supply Shocks on the US Economy. Christiane Baumeister Ghent University Gert Peersman Ghent University. Motivation. The dynamic effects of oil supply shocks on the economy have probably changed over time

By Renfred
(417 views)

Bonds and Swaps

Bonds and Swaps

FIN285a: Lecture 5.1a Fall 2008. Bonds and Swaps. Outline. Coupon bonds Currency swap Fixed/floating swaps. Software. bondvar.m bpswaphist.m bpswapbs.m fixfloat.m. Bond Pricing: Assumptions. Flat term structure Yields Geometric random walk Rate = Tbond + 5% (risk spread)

By DoraAna
(646 views)

Phenotypic factor analysis Conor V. Dolan & Abdel Abdellaoui

Phenotypic factor analysis Conor V. Dolan & Abdel Abdellaoui

Phenotypic factor analysis Conor V. Dolan & Abdel Abdellaoui Biologische Psychologie, VU, Amsterdam Boulder Workshop - March 2016. Phenotypic factor analysis.

By Jimmy
(1047 views)

Summarizing Variation

Summarizing Variation

Summarizing Variation. Michael C Neale PhD Virginia Institute for Psychiatric and Behavioral Genetics Virginia Commonwealth University. Overview. Mean Variance Covariance Not always necessary/desirable. Computing Mean. Formula E (x i )/N Can compute with Pencil Calculator SAS SPSS

By arleen
(364 views)

CJT 765: Structural Equation Modeling

CJT 765: Structural Equation Modeling

CJT 765: Structural Equation Modeling. Class 6: fitting a model, fit indices, comparing models, statistical power . Outline of Class. Finishing up Identification Issues Rules for Assessing Identification Problems, Prevention, and Tests Fixing Identification Problems

By andrew
(236 views)

Learning Leadership Matters: Teachers’ experiences of innovatively and conventionally prepared principals

Learning Leadership Matters: Teachers’ experiences of innovatively and conventionally prepared principals

Learning Leadership Matters: Teachers’ experiences of innovatively and conventionally prepared principals. Paper presented by: Terry Orr, Bank Street College Stelios Orphanos, Stanford University AERA, Chicago, April 2007. Purpose.

By Antony
(275 views)

I. Sample Geometry and Random Sampling

I. Sample Geometry and Random Sampling

I. Sample Geometry and Random Sampling. A. The Geometry of the Sample Our sample data in matrix form looks like this:. Separate multivariate observations.

By eze
(371 views)

Advanced Statistics

Advanced Statistics

Advanced Statistics. Factor Analysis, I I. Last lecture. 1. What causes what, ξ → Xs, Xs→ ξ ? 2. Do we explore the relation of Xs to ξs, or do we test (try to confirm) our a priori assumption about this relation?

By maida
(677 views)

Face Recognition in the Infrared Spectrum

Face Recognition in the Infrared Spectrum

Face Recognition in the Infrared Spectrum. Prof. Ioannis Pavlidis. COSC 6397. U of H. Primary Applications. Biometric Identification Passwords/PINs. Tokens (like ID cards). You can be your own password. Surveillance

By saxon
(109 views)

A Note on Modeling the Covariance Structure in Longitudinal Clinical Trials

A Note on Modeling the Covariance Structure in Longitudinal Clinical Trials

A Note on Modeling the Covariance Structure in Longitudinal Clinical Trials. Devan V. Mehrotra Merck Research Laboratories, Blue Bell, PA FDA/Industry Statistics Workshop September 18, 2003. Outline. Comparative clinical trial Typical questions of interest Standard analysis

By machiko
(133 views)

Principal Components Analysis Babak Rasolzadeh Tuesday, 5th December 2006

Principal Components Analysis Babak Rasolzadeh Tuesday, 5th December 2006

Principal Components Analysis Babak Rasolzadeh Tuesday, 5th December 2006. Example: 53 Blood and urine measurements (wet chemistry) from 65 people (33 alcoholics, 32 non-alcoholics). Matrix Format. Spectral Format. Data Presentation. Data Presentation. Univariate. Bivariate. Trivariate.

By meli
(159 views)

Portfolio Diversity and Robustness

Portfolio Diversity and Robustness

Portfolio Diversity and Robustness. TOC. Markowitz Model Diversification Robustness Random returns Random covariance Extensions Conclusion. Introduction & Background. The classic model S - Covariance matrix (deterministic) r – Return vector (deterministic) Solution via KKT conditions.

By tarannum
(111 views)

Spectral Methods

Spectral Methods

Spectral Methods. Tutorial 6. © Maks Ovsjanikov tosca.cs.technion.ac.il/book. Numerical geometry of non-rigid shapes Stanford University, Winter 2009. Outline.

By gur
(192 views)

Uncertainty Estimation

Uncertainty Estimation

Uncertainty Estimation. Simon Cousens and Richard Silverwood. Overview. an implementation/coding issue with the Loess approach accounting for within-source correlation increasing uncertainty with increasing sources(?). Implementation issue.

By ismet
(160 views)

Multi-site Performance Monitoring in Batch Pharmaceutical Production

Multi-site Performance Monitoring in Batch Pharmaceutical Production

Multi-site Performance Monitoring in Batch Pharmaceutical Production. Chris Wong Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle. Presentation Structure.

By nyx
(261 views)

Semivariogram Analysis and Estimation

Semivariogram Analysis and Estimation

Semivariogram Analysis and Estimation. Tanya , Nick Caroline. Semivariogram. Gives information about the nature and structure of spatial dependency in a random field → must be estimated from the data Estimating a semivariogram: Derive empirical estimate from data

By kaycee
(304 views)

Structural Equation Modeling

Structural Equation Modeling

Structural Equation Modeling. Continued: Lecture 2 Psy 524 Ainsworth. Covariance Algebra. Underlying parameters in SEM Regression Coefficients Variances and Covariances A hypothesized model is used to estimate these parameters for the population (assuming the model is true)

By scott
(365 views)

Today’s Lecture

Today’s Lecture

Today’s Lecture. One more test for normality Shapiro-Wilk Test Testing variances Equality of Variance via the F-Distribution Levene’s Test for Equality of Variances. Reference Material. Shapiro and Wilk, 1965. Biometrika (52:3 and 4) pgs. 591-611. Burt and Barber, page 325

By lenci
(249 views)

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