'Covariance matrices' diaporamas de présentation

Segmentation and Clustering

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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

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Statistics for Microarrays

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Improved Full Bayesian Evaluation of Neutron- induced Reactions on 55 Mn

Improved Full Bayesian Evaluation of Neutron- induced Reactions on 55 Mn. H. Leeb, St. Gundacker , D. Neudecker, Th . Srdinko , V. Wildpaner Atominstitut, Vienna University of Technology, Vienna, Austria. EFNUDAT 2010, CERN ID29.

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Multivariate volatility models

Multivariate volatility models. Nimesh Mistry Filipp Levin. Introduction. Why study multivariate models The models: BEKK CCC DCC Conditional correlation forecasts Results Interpretation and Conclusion. Motivation .

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a factor model for learning higher-order features in natural images yan karklin ( nyu + hhmi)

a factor model for learning higher-order features in natural images yan karklin ( nyu + hhmi) joint work with mike lewicki ( case western reserve u ) icml workshop on learning feature hierarchies - june 18, 2009. retina. LGN. V2. V1. non-linear processing in the early visual system.

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Outline

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Variational data assimilation for morphodynamic model parameter estimation

Variational data assimilation for morphodynamic model parameter estimation. Department of Mathematics, University of Reading: Polly Smith *, Sarah Dance, Mike Baines, Nancy Nichols, Environmental Systems Science Centre, University of Reading: Tania Scott *email: p.j.smith@reading.ac.uk.

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Estimating Growth when Content Specifications Change:

Estimating Growth when Content Specifications Change:. A Multidimensional IRT Approach Mark D. Reckase Tianli Li Michigan State University. The Problem. State curriculum frameworks often change from one grade to the next reflecting the addition of new instructional content.

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Leveraging Category-Level Labels For Instance-Level Image Retrieval

Leveraging Category-Level Labels For Instance-Level Image Retrieval. Outline. Introduction Learning techniques Experiment Conclusion. Outline. Introduction Learning techniques Experiment Conclusion. Introduction. The problem query-by-example instance level

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PRESENTER Greg M. Ilag Executive Director International Leadership Advisory Group, New York

International Conference on Governance Across Ethics, Culture, and Citizenship (What to give and what to expect). PRESENTER Greg M. Ilag Executive Director International Leadership Advisory Group, New York DBA Candidate, University of Phoeni x. RESEARCH TOPIC.

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Cross-section Covariance Data in BROND-2.2

Review of Available Cross Section Covariance Matrices and their use in the scope of reactor physics benchmarks. Cross-section Covariance Data in BROND-2.2. File types are MF= 33 (cross sections) The Nuclides are: C-0, Au-197, Pb-0. Cross-section Covariance Data in CENDL-2.1.

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Neural Firing

Neural Firing. Notation I. x(t)=signal vector; N(t)=#spikes fired up to time t; H(k)=[? 1:k-1 ,x 1:k ,N 1:k ] ? t[k],t[k]+ ?t[k] =likelihood over interval t k , t k +?t k,i ?t k,i ~ interval: t k +? i=1:j-1 ?t k,j , t k + ? i=1:j ?t k,j ,. Fact.

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Details on Kalman Filter and Smoothing Code for Chapter 6

Details on Kalman Filter and Smoothing Code for Chapter 6 In astsa , there are three levels (0,1,2) of filtering and smoothing, Kfilter0/Ksmooth0, Kfilter1/Ksmooth1, Kfilter2/Ksmooth2.

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State space model/approach. Deals with many situations: missing values

State space model/approach. Deals with many situations: missing values fitting ARIMAs by mle irregularly spaced data aggregation/ disagregation measurement error outliers structural breaks prediction, smoothing nonlinearity

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Quantifying the Sources of Inter-Model Spread in Climate Sensitivity

Quantifying the Sources of Inter-Model Spread in Climate Sensitivity. Peter Caldwell, Mark Zelinka , Karl Taylor, and Kate Marvel LLNL. Prepared by LLNL under Contract DE-AC52-07NA27344. LLNL-PRES-661065. Motivation.

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Tilt Correction to Geometric Hourglass Effect

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Nonparametric low-rank tensor imputation

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MANOVA

MANOVA. Control of experimentwise error rate (problem of multiple tests). Detection of multivariate vs. univariate differences among groups (multivariate responses). Analysis of repeated measures (e.g. growth curves). Multivariate differences. Partition of SS. MANOVA.

343 views • 6 slides