'Model evidence' diaporamas de présentation

DCM: Advanced Topics

DCM: Advanced Topics. Klaas Enno Stephan Translational Neuromodeling Unit (TNU) Institute for Biomedical Engineering, University of Zurich & ETH Zurich Laboratory for Social & Neural Systems Research (SNS), University of Zurich

712 views • 58 slides

Bayesian Inference

Bayesian Inference. Will Penny. Wellcome Centre for Neuroimaging, UCL, UK. SPM for fMRI Course, London, October 21st, 2010. What is Bayesian Inference ?. (From Daniel Wolpert). Bayesian segmentation and normalisation. realignment. smoothing. general linear model. Gaussian

752 views • 56 slides

Bayesian Inference

Bayesian Inference. Will Penny. Wellcome Centre for Neuroimaging, UCL, UK. SPM for fMRI Course, London, October 21st, 2010. What is Bayesian Inference ?. (From Daniel Wolpert). Bayesian segmentation and normalisation. realignment. smoothing. general linear model. Gaussian

763 views • 56 slides

Bayesian Inference

Bayesian Inference. Will Penny. Wellcome Centre for Neuroimaging, UCL, UK. SPM for fMRI Course, London, October 21st, 2010. What is Bayesian Inference ?. (From Daniel Wolpert). Bayesian segmentation and normalisation. realignment. smoothing. general linear model. Gaussian

594 views • 56 slides

DCM: Advanced issues

DCM: Advanced issues. Klaas Enno Stephan Laboratory for Social & Neural Systems Research Institute for Empirical Research in Economics University of Zurich Functional Imaging Laboratory (FIL) Wellcome Trust Centre for Neuroimaging University College London.

659 views • 53 slides

Friday 22 nd Oct. 2010 SPM fMRI course Wellcome Trust Centre for Neuroimaging London

618 views • 50 slides

Bayesian inference

Bayesian inference. Lee Harrison York Neuroimaging Centre 23 / 10 / 2009. Posterior probability maps (PPMs). Spatial priors on activation extent. Bayesian segmentation and normalisation. Dynamic Causal Modelling. realignment. smoothing. general linear model. Gaussian field theory.

697 views • 47 slides

DYNAMIC CAUSAL MODELLING FOR fMRI Theory and Practice

DYNAMIC CAUSAL MODELLING FOR fMRI Theory and Practice. Diego Lorca Puls and Sotirios Polychronis. OUTLINE. DCM: Theory Background Basis of DCM Neuronal Model Hemodynamic Model Model Inversion: Parameter Estimation, Model Comparison and Selection DCM Implementation Alternatives

463 views • 46 slides

DCM: Advanced topics

DCM: Advanced topics. Klaas Enno Stephan Laboratory for Social & Neural Systems Research Institute for Empirical Research in Economics University of Zurich Wellcome Trust Centre for Neuroimaging Institute of Neurology University College London. SPM Short Course FIL, 22 October 2010.

460 views • 45 slides

Bayesian inference

Bayesian inference. Lee Harrison York Neuroimaging Centre 01 / 05 / 2009. Overview. Probabilistic modeling and representation of uncertainty Introduction Curve fitting without Bayes Bayesian paradigm Hierarchical models Variational methods (EM, VB) SPM applications

591 views • 39 slides

DCM: Advanced issues

DCM: Advanced issues. Klaas Enno Stephan Laboratory for Social & Neural Systems Research Institute for Empirical Research in Economics University of Zurich Functional Imaging Laboratory (FIL) Wellcome Trust Centre for Neuroimaging University College London.

478 views • 39 slides

DCM for fMRI: Advanced topics

DCM for fMRI: Advanced topics. Klaas Enno Stephan Laboratory for Social & Neural Systems Research (SNS) University of Zurich Wellcome Trust Centre for Neuroimaging University College London. SPM Course, London 13 May 2011. Dynamic Causal Modeling (DCM).

398 views • 39 slides

On the intimate relationship between functional and effective connectivity

On the intimate relationship between functional and effective connectivity Karl Friston, Wellcome Centre for Neuroimaging.

516 views • 37 slides

The M/EEG inverse problem

375 views • 37 slides

On the intimate relationship between functional and effective connectivity

On the intimate relationship between functional and effective connectivity Karl Friston, Wellcome Centre for Neuroimaging.

376 views • 37 slides

Bayesian Model Comparison

SPC. V1. V5. SPC. V1. V5. Bayesian Model Comparison. Will Penny. Wellcome Centre for Neuroimaging, UCL, UK. London-Marseille Joint Meeting, Institut de Neurosciences Cognitive de la Mediterranee, Marseille, September 28-29, 2009. Overview. Priors, likelihoods and posteriors

445 views • 35 slides

Bayesian Model Comparison

SPC. V1. V5. SPC. V1. V5. Bayesian Model Comparison. Will Penny. Wellcome Centre for Neuroimaging, UCL, UK. London-Marseille Joint Meeting, Institut de Neurosciences Cognitive de la Mediterranee, Marseille, September 28-29, 2009. Overview. Priors, likelihoods and posteriors

358 views • 35 slides

The principles of DCM

The principles of DCM. Vladimir Litvak. Wellcome Trust Centre for Neuroimaging. Epigraph. What I cannot create I do not understand. Richard P. Feynman. What do we need for DCM?. Data (possibly data features). 250. 0. input. depolarization. 1 st and 2d order moments. 200. -20.

368 views • 35 slides

C. Lu 1 , T. Van Gestel 1 , J. A. K. Suykens 1 , S. Van Huffel 1 , D. Timmerman 2 , I. Vergote 2

Preoperative Prediction of Malignancy of Ovarian Tumors Using Least Squares Support Vector Machines. C. Lu 1 , T. Van Gestel 1 , J. A. K. Suykens 1 , S. Van Huffel 1 , D. Timmerman 2 , I. Vergote 2 1 Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium,

348 views • 24 slides