'Bayesian framework' diaporamas de présentation

Probabilistic Machine Learning Approaches to Medical Classification Problems Chuan LU

Probabilistic Machine Learning Approaches to Medical Classification Problems Chuan LU. Jury: Prof. L. Froyen, chairman Prof. J. Vandewalle Prof. S. Van Huffel, promotor Prof. J. Beirlant Prof. J.A.K. Suykens, promotor Prof. P.J.G. Lisboa Prof. D. Timmerman Prof. Y. Moreau

645 views • 45 slides

Monte Carlo Maximum Likelihood Methods for Estimating Uncertainty Arising from Shared Errors in Exposures in Epidemiolog

Monte Carlo Maximum Likelihood Methods for Estimating Uncertainty Arising from Shared Errors in Exposures in Epidemiological Studies. Daniel O. Stram University of Southern California. Complex Dosimetry Systems: a Working Definition (my definition).

583 views • 42 slides

Meta-Cognition, Motivation, and Affect

Meta-Cognition, Motivation, and Affect. PSY504 Spring term, 2011 January 26, 2010. Help-Seeking. Model of Help-Seeking ( Aleven et al., 2003). 1. Become aware of need for help 2. Decide to seek help 3. Identify potential helper(s ) 4. Use strategies to elicit help

522 views • 41 slides

Network biology (& predicting gene function)

599 views • 39 slides

Space-Time Modelling to support local policing

Space-Time Modelling to support local policing. Robert Haining Department of Geography, University of Cambridge, England. AAG; New York; Feb 2012. INTRODUCTION Recent trends in policing in the UK (Crime and Disorder Act 1998):

529 views • 38 slides

Bayesian Inference and Posterior Probability Maps

Bayesian Inference and Posterior Probability Maps . Guillaume Flandin. Wellcome Department of Imaging Neuroscience, University College London, UK. SPM Course, London, May 2005. Overview. Introduction Bayesian inference Segmentation and Normalisation Gaussian Prior and Likelihood

739 views • 33 slides

606 views • 30 slides

Christopher M. Bishop

616 views • 30 slides

Markov Random Fields with Efficient Approximations

Markov Random Fields with Efficient Approximations. Yuri Boykov, Olga Veksler, Ramin Zabih Computer Science Department CORNELL UNIVERSITY. Introduction. MAP-MRF approach (Maximum Aposteriori Probability estimation of MRF)

576 views • 30 slides

Overview

Statistical Parametric Map. Design matrix. fMRI time-series. kernel. Motion correction. Smoothing. General Linear Model. Spatial normalisation. Parameter Estimates. Standard template. Overview. The preprocessing sequence revisted. Realignment

376 views • 28 slides

The M/EEG inverse problem and solutions

The M/EEG inverse problem and solutions. Gareth R. Barnes. Format. The inverse problem Choice of prior knowledge in some popular algorithms Why the solution is important. Magnetic field. MEG pick-up coil. Electrical potential difference (EEG). scalp. skull. cortex. Volume currents.

636 views • 26 slides

MEG/EEG Inverse problem and solutions In a Bayesian Framework

255 views • 25 slides

Using Prior Information in Bayesian Inference - with Application to Fault Diagnosis

319 views • 19 slides

A Bayesian framework for optimal utilization of plant-pollinator interaction data

Getting the most out of insect-related data. A Bayesian framework for optimal utilization of plant-pollinator interaction data. Background. A major issue for pollinator studies is to find out what affects the number of various insects.

241 views • 17 slides