'Latent variables' presentation slideshows

Measuring Uncertainty in Graph Cut Solutions

Measuring Uncertainty in Graph Cut Solutions. Pushmeet Kohli Philip H.S. Torr Department of Computing Oxford Brookes University. st-mincut. Most Probable Solution. s. t. Labelling problem. Objective. Graph Cut. No uncertainty measure associated with the solution.

947 views • 73 slides

Christopher M. Bishop

Latent Variables, Mixture Models and EM. Christopher M. Bishop. Microsoft Research, Cambridge. BCS Summer School Exeter, 2003. Overview. K-means clustering Gaussian mixtures Maximum likelihood and EM Latent variables: EM revisited Bayesian Mixtures of Gaussians . Old Faithful.

788 views • 55 slides

Gaussian Process Structural Equation Models with Latent Variables

Gaussian Process Structural Equation Models with Latent Variables. Ricardo Silva Department of Statistical Science University College London Robert B. Gramacy Statistical laboratory University of Cambridge. ricardo@stats.ucl.ac.uk. bobby@statslab.cam.ac.uk. Summary.

704 views • 52 slides

General Structural Equations (LISREL)

640 views • 46 slides

Random effect modelling of great tit nesting behaviour

Random effect modelling of great tit nesting behaviour. William Browne (University of Nottingham) In collaboration with Richard Pettifor (Institute of Zoology, London), Robin McCleery and Ben Sheldon (University of Oxford). Talk Summary. Background. Wytham woods great tit dataset.

794 views • 42 slides

ECONOMETRIC MODELING OF TECHNICAL CHANGE by Dale W. Jorgenson Harvard University Presented to the World Input-Output Da

464 views • 33 slides

Module 2: Bayesian Hierarchical Models

634 views • 32 slides

Educational Opportunity & Genetics

427 views • 31 slides

Chapter 16

485 views • 30 slides

Default Correlation: Empirical Evidence

Default Correlation: Empirical Evidence. Arnaud de Servigny & Olivier Renault. Agenda. Do correlations matter ? Estimating default correlations empirically. Are equity correlations good proxies for asset correlations ? Correlations and the business cycle.

654 views • 28 slides

Latent Growth Curve Models

Latent Growth Curve Models. Patrick Sturgis, Department of Sociology, University of Surrey. Overview. Random effects as latent variables Growth parameters Specifying time in LGC models Linear Growth Non-linear growth Explaining Growth Fixed and time-varying predictors

1k views • 26 slides

Learning and Vision: Generative Methods

Learning and Vision: Generative Methods. Andrew Blake, Microsoft Research Bill Freeman, MIT ICCV 2003 October 12, 2003 . Learning and vision: Generative Methods. Machine learning is an important direction in computer vision. Our goal for this class: Give overviews of useful techniques.

548 views • 26 slides

Infinite Hidden Relational Models

Infinite Hidden Relational Models. Zhao Xu 1 , Volker Tresp 2 , Kai Yu 2 , Shipeng Yu and Hans-Peter Kriegel 1 1 University of Munich, Germany 2 Siemens Corporate Technology, Munich, Germany. Motivation.

409 views • 25 slides

Stages in Structural Equation Modeling

Stages in Structural Equation Modeling. The Stages in Structural Equation Model. Stage 1) Assessing individual constructs Stage 2) Developing and assessing the measurement model validity Stage 3) Specifying the structural model Stage 4) Assessing structural model validity.

473 views • 24 slides

Reading and writing raw data files:

367 views • 20 slides

Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensity

Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensity. by. Ryan P. Adams, Iain Murray, and David J.C. MacKay. (ICML 2009). Presented by Lihan He ECE, Duke University July 31, 2009. Outline. Introduction The model Poisson distribution

376 views • 19 slides

Social Class and Oral Health. A comparison between the 1988 and 1998 Adult Oral Health Surveys

404 views • 17 slides

Correlated Topic Models By Blei and Lafferty (NIPS 2005)

Correlated Topic Models By Blei and Lafferty (NIPS 2005). Presented by Chunping Wang ECE, Duke University August 4 th , 2006. Outlines. Introduction Latent Dirichlet Allocation (LDA) Correlated Topic Models (CTM) Experimental Results Conclusions. Introduction(1).

449 views • 16 slides

Limited Dependent Variables

Limited Dependent Variables. P ( y = 1| x ) = G ( b 0 + x b ) y* = b 0 + x b + u, y = max ( 0,y* ). Binary Dependent Variables. Recall the linear probability model, which can be written as P( y = 1| x ) = b 0 + x b

331 views • 15 slides

Deep Learning

Deep Learning. Deep learning?. MLP-> NN-> Deep Learning Deep networks trained with backpropagation (without unsupervised pretraining ) perform worse than shallow networks Supervised/unsupervised Each layer. Advantage. Deep Architectures can be representationally efficient

514 views • 10 slides