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growing desire for transparency within catastrophe models a need to better quantify uncertainty and the requirement to improve under standing of non modelled perils hav e exacerbated the demand for t

growing desire for transparency within catastrophe models a need to better quantify uncertainty and the requirement to improve under standing of non modelled perils hav e exacerbated the demand for t

  • lindy-dunigan
  • 2 Slides

In response Impact Forecasting RQ57347HQILHOG5752...

Random Complex Zeroes Mikhail Sodin Abstract Random complex zeroes a

Random Complex Zeroes Mikhail Sodin Abstract Random complex zeroes a

  • sherrill-nordquist
  • 1 Slides

ka chaotic analytic zero point s are zeroes of a G...

MONITORING IN CREDIT INSTITUTIONS COMPARATIVE APPROACH ON INTERNAL CONTROL SYSTEMS THE CASE OF ROMANIA VS

MONITORING IN CREDIT INSTITUTIONS COMPARATIVE APPROACH ON INTERNAL CONTROL SYSTEMS THE CASE OF ROMANIA VS

  • conchita-marotz
  • 6 Slides

INTERNATIONAL MODELS OF CONTROL 350tef259nescu Cr...

Child Protection Program Logic Models for Supervisors Background Minnesota was one of the first states to participate in the federal Child and Family Service Reviews

Child Protection Program Logic Models for Supervisors Background Minnesota was one of the first states to participate in the federal Child and Family Service Reviews

  • conchita-marotz
  • 18 Slides

The review uses multiple sources to assess a stat...

Modeling Human Locomotion with Topologically Constrained Latent Variable Models Raquel Urtasun David J

Modeling Human Locomotion with Topologically Constrained Latent Variable Models Raquel Urtasun David J

  • yoshiko-marsland
  • 15 Slides

Fleet and Neil D Lawrence Massachusetts Institut...

Higherorder Autoregressive Models for Dynamic Textures Midori Hyndman Allan Jepson David Fleet Department of Computer Science University of Toronto mhyndmanjepsonfleetcs

Higherorder Autoregressive Models for Dynamic Textures Midori Hyndman Allan Jepson David Fleet Department of Computer Science University of Toronto mhyndmanjepsonfleetcs

  • natalia-silvester
  • 10 Slides

torontoedu Abstract Dynamic textured sequences are...

Lecture Intro and Snooping Protocols Topics multicore cache organizations programming models cache coherence snoopingbased MultiCore Cache Organizations Private L caches Shared L cache Bus between

Lecture Intro and Snooping Protocols Topics multicore cache organizations programming models cache coherence snoopingbased MultiCore Cache Organizations Private L caches Shared L cache Bus between

  • kittie-lecroy
  • 23 Slides

Message Passing Sharedmemory single copy of share...

Structured Models for FinetoCoarse Sentiment Analysis Ryan McDonald Kerry Hannan Tyler Neylon Mike Wells Jeff Reynar Google Inc

Structured Models for FinetoCoarse Sentiment Analysis Ryan McDonald Kerry Hannan Tyler Neylon Mike Wells Jeff Reynar Google Inc

  • mitsue-stanley
  • 8 Slides

76 Ninth Avenue New York NY 10011 Contact email r...

STATE MODELS AND THE JONES POLYNOMIAL friced in recisrdfirm I Srpremher

STATE MODELS AND THE JONES POLYNOMIAL friced in recisrdfirm I Srpremher

  • debby-jeon
  • 13 Slides

INTRODIJCTION IN THIS PAPER I construct a state m...

October Kevin Costello Random Walks on Directed Graphs Comments on a Method of Santosh Ventala The Eigencluster Algorithm given by Santosh Ventala is essentially a recursive algorithm it uses spec

October Kevin Costello Random Walks on Directed Graphs Comments on a Method of Santosh Ventala The Eigencluster Algorithm given by Santosh Ventala is essentially a recursive algorithm it uses spec

  • pasty-toler
  • 5 Slides

This may be inef64257cient as you are forced to k...

Heuristic and Linear Models of Judgment Matching Rules and Environments Robin M

Heuristic and Linear Models of Judgment Matching Rules and Environments Robin M

  • cheryl-pisano
  • 26 Slides

Hogarth ICREA and Universitat Pompeu Fabra Natali...

Learning Loworder Models for Enforcing Highorder Statistics Patrick Pletscher Pushmeet Kohli ETH Zurich Zurich Switzerland Microsoft Research Cambridge UK Abstract Models such as pairwise conditional

Learning Loworder Models for Enforcing Highorder Statistics Patrick Pletscher Pushmeet Kohli ETH Zurich Zurich Switzerland Microsoft Research Cambridge UK Abstract Models such as pairwise conditional

  • min-jolicoeur
  • 9 Slides

However they have limited expressive power and of...

Efciently Matching Sets of Features with Random Histograms Wei Dong Zhe Wang Moses Charikar Kai Li Department of Computer Science Princeton University Olden Street Princeton NJ USA wdongzhewangmose

Efciently Matching Sets of Features with Random Histograms Wei Dong Zhe Wang Moses Charikar Kai Li Department of Computer Science Princeton University Olden Street Princeton NJ USA wdongzhewangmose

  • alida-meadow
  • 10 Slides

princetonedu ABSTRACT As the commonly used represe...

On Invariance in Hierarchical Models Jake Bouvrie Lorenzo Rosasco and Tomaso Poggio Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge MA USA jvblrosasco

On Invariance in Hierarchical Models Jake Bouvrie Lorenzo Rosasco and Tomaso Poggio Center for Biological and Computational Learning Massachusetts Institute of Technology Cambridge MA USA jvblrosasco

  • giovanna-bartolotta
  • 9 Slides

edu tpaimitedu Abstract A goal of central importan...

Chapter Giant Sucking Sound Lyapunov Functions A system whether it be social biological or physical can go to equilibrium it can cycle it can produce random patterns or it can be complex

Chapter Giant Sucking Sound Lyapunov Functions A system whether it be social biological or physical can go to equilibrium it can cycle it can produce random patterns or it can be complex

  • liane-varnes
  • 11 Slides

In this chapter were going to learn one reason wh...

depmixS An Package for Hidden Markov Models Ingmar Visser University of Amsterdam Maarten Speekenbrink University College London Abstract This introduction to the package depmixS is a slightly modie

depmixS An Package for Hidden Markov Models Ingmar Visser University of Amsterdam Maarten Speekenbrink University College London Abstract This introduction to the package depmixS is a slightly modie

  • lindy-dunigan
  • 21 Slides

Please refer to that article when using depmixS4 ...

Bayesian Nonparametric Models Peter Orbanz Cambridge University Yee Whye Teh University College London Related keywords Bayesian Methods Prior Probabilities Dirichlet Process Gaussian Processes

Bayesian Nonparametric Models Peter Orbanz Cambridge University Yee Whye Teh University College London Related keywords Bayesian Methods Prior Probabilities Dirichlet Process Gaussian Processes

  • natalia-silvester
  • 14 Slides

De64257nition A Bayesian nonparametric model is a...

Numerical models of broadbandwidth nanosecond optical parametric oscillators A

Numerical models of broadbandwidth nanosecond optical parametric oscillators A

  • tatiana-dople
  • 11 Slides

V Smith and Russell J Gehr Department 1128 Lasers...

Fourier Series for Accurate Stable ReducedOrder Models for Linear CFD Applications

Fourier Series for Accurate Stable ReducedOrder Models for Linear CFD Applications

  • stefany-barnette
  • 11 Slides

K Willcox and A Megretski Massachusetts Institute...

Hidden semiMark models HSMMs vin Murphy www

Hidden semiMark models HSMMs vin Murphy www

  • faustina-dinatale
  • 13 Slides

aimitedu murphyk 20 No ember 2002 Abstract semiMar...

MONITORING IN CREDIT INSTITUTIONS COMPARATIVE APPROACH ON INTERNAL CONTROL SYSTEMS THE CASE OF ROMANIA VS

MONITORING IN CREDIT INSTITUTIONS COMPARATIVE APPROACH ON INTERNAL CONTROL SYSTEMS THE CASE OF ROMANIA VS

  • jane-oiler
  • 6 Slides

INTERNATIONAL MODELS OF CONTROL 350tef259nescu Cr...

Abstract This paper models an electric variable valve timing VVT system and develops the corresponding controller for the electric VVT system

Abstract This paper models an electric variable valve timing VVT system and develops the corresponding controller for the electric VVT system

  • jane-oiler
  • 6 Slides

The studied electric V VT uses a planetary gear s...

Predicting nancial markets with Go ogle Trends and not so random keywords Damien Challet Chaire de nance quantitative Laboratoire de mathmatiques appliques aux systmes cole Centrale Paris Grande Voie

Predicting nancial markets with Go ogle Trends and not so random keywords Damien Challet Chaire de nance quantitative Laboratoire de mathmatiques appliques aux systmes cole Centrale Paris Grande Voie

  • pasty-toler
  • 9 Slides

We rst review the many subtle and less subtle bia...

Smoothed Bloom lter language models TeraScale LMs on the Cheap David Talbot and Miles Osborne School of Informatics University of Edinburgh Buccleuch Place Edinburgh EH LW UK d

Smoothed Bloom lter language models TeraScale LMs on the Cheap David Talbot and Miles Osborne School of Informatics University of Edinburgh Buccleuch Place Edinburgh EH LW UK d

  • natalia-silvester
  • 9 Slides

rtalbotsmsedacuk milesinfedacuk Abstract A Bloom 6...

D eople racking with Gaussian Pr ocess Dynamical Models Raquel Urtasun Computer ision Laboratory EPFL Switzerland raquel

D eople racking with Gaussian Pr ocess Dynamical Models Raquel Urtasun Computer ision Laboratory EPFL Switzerland raquel

  • alexa-scheidler
  • 8 Slides

urtasunep57347ch Da vid J Fleet Dept of Computer S...

Feature Detection and Tracking with Constrained Local Models David Cristinacce and Tim Cootes Dept

Feature Detection and Tracking with Constrained Local Models David Cristinacce and Tim Cootes Dept

  • debby-jeon
  • 10 Slides

Imaging Science and Biomedical Engineering Univer...

COMPRESSIVE SENSING BY RANDOM CONVOLUTION JUSTIN ROMBERG Abstract

COMPRESSIVE SENSING BY RANDOM CONVOLUTION JUSTIN ROMBERG Abstract

  • mitsue-stanley
  • 22 Slides

This paper outlines a new framework for compressi...

On Smoothing and Inference for Topic Models Arthur Asuncion Max Welling Padhraic Smyth Department of Computer Science University of California Irvine Irvine CA USA asuncionwellingsmyth ics

On Smoothing and Inference for Topic Models Arthur Asuncion Max Welling Padhraic Smyth Department of Computer Science University of California Irvine Irvine CA USA asuncionwellingsmyth ics

  • natalia-silvester
  • 8 Slides

uciedu Yee Whye Teh Gatsby Computational Neuroscie...

Visual Models of Plants Interacting with Their Environment Radomir Mech and Przemyslaw Prusinkiewicz Department of Computer Science University of Calgary Calgary Alberta Canada TN N email mechpwpcpsc

Visual Models of Plants Interacting with Their Environment Radomir Mech and Przemyslaw Prusinkiewicz Department of Computer Science University of Calgary Calgary Alberta Canada TN N email mechpwpcpsc

  • myesha-ticknor
  • 15 Slides

ucalgaryca Abstract Interaction with the environme...

A Comparative Study of Energy Minimization Methods for Markov Random Fields Richard Szeliski RaminZabih DanielScharstein Olga Veksler Vladimir Kolmogorov Aseem Agarwala Marshall Tappen and Carsten

A Comparative Study of Energy Minimization Methods for Markov Random Fields Richard Szeliski RaminZabih DanielScharstein Olga Veksler Vladimir Kolmogorov Aseem Agarwala Marshall Tappen and Carsten

  • liane-varnes
  • 14 Slides

com Cornell University rdzcscornelledu Middlebury ...

INTRODUCTION TO DIGITAL FILTERS Analog and digital filters In signal processing the function of a filter is to remove unwanted parts of the signal such as random noise or to extract useful parts of t

INTRODUCTION TO DIGITAL FILTERS Analog and digital filters In signal processing the function of a filter is to remove unwanted parts of the signal such as random noise or to extract useful parts of t

  • danika-pritchard
  • 15 Slides

The following block diagram illustrates the basic...

NI LabVIEW System Identification Toolkit NEW System Identication Toolkit Identify dynamic system models directly from realworld stimulus and response signals Use realworld data to calibrate simulatio

NI LabVIEW System Identification Toolkit NEW System Identication Toolkit Identify dynamic system models directly from realworld stimulus and response signals Use realworld data to calibrate simulatio

  • yoshiko-marsland
  • 3 Slides

6 or later Full or Pro Overview The NI LabVIEW Sys...

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

SIGN DETECTION IN NA TU RAL IM GES WITH CONDITIONAL RANDOM FIELDS Jero einman Allen Hanson and Andrew McCallum weinmanhansonmccallumcs

  • jane-oiler
  • 10 Slides

Departmen of Computer Science Univ ersit of Massa...

Cascade Object Detection with Deformable Part Models Pedro F

Cascade Object Detection with Deformable Part Models Pedro F

  • alexa-scheidler
  • 8 Slides

Felzenszwalb University of Chicago pffcsuchicagoe...

Models IJID No of Pages Please cite this article in press as Ruan SY et al

Models IJID No of Pages Please cite this article in press as Ruan SY et al

  • natalia-silvester
  • 3 Slides

Catheterrelated fungemia caused by Candida interm...

Chapter Part II Autoregressive Models Another simple time series model is the f irst order autoregression denoted by AR

Chapter Part II Autoregressive Models Another simple time series model is the f irst order autoregression denoted by AR

  • stefany-barnette
  • 5 Slides

Th eries is AR1 if it satis64257es the iterativ...

Sparse Recovery Using Sparse Matrices Anna Gilbert Piotr Indyk Abstract We survey algorithms for sparse recovery prob lems that are based on sparse random matrices

Sparse Recovery Using Sparse Matrices Anna Gilbert Piotr Indyk Abstract We survey algorithms for sparse recovery prob lems that are based on sparse random matrices

  • briana-ranney
  • 10 Slides

Such matrices has several attractive properties t...

AIDED LANGUAGE STIMULATION Aided language stimulation ALS is a communication strategy where a communication partner teaches symbol meaning and models language by combining his or her own verbal input

AIDED LANGUAGE STIMULATION Aided language stimulation ALS is a communication strategy where a communication partner teaches symbol meaning and models language by combining his or her own verbal input

  • kittie-lecroy
  • 2 Slides

This is done by simultaneously selecting vocabula...

Item Measure Models Ultrashift PLUS MHP Ultrashift PLUS MXP Ultrashift PLUS LAS Ultrashift PLUS LAS Ultrashift PLUS LSE Advantage Series FASA Advantage Series FASA Advantage Series FASA Advantage Ser

Item Measure Models Ultrashift PLUS MHP Ultrashift PLUS MXP Ultrashift PLUS LAS Ultrashift PLUS LAS Ultrashift PLUS LSE Advantage Series FASA Advantage Series FASA Advantage Series FASA Advantage Ser

  • yoshiko-marsland
  • 1 Slides

978 978 915 915 978 Input Torque Capacity lbft 14...

Statistics StateSpace Models Overview

Statistics StateSpace Models Overview

  • pasty-toler
  • 12 Slides

Statespace models aka dynamic linear models DLM...

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