'Bayesian model selection' diaporamas de présentation

Reconstructing gene regulatory networks with probabilistic models

Reconstructing gene regulatory networks with probabilistic models. Dirk Husmeier. Marco Grzegorczyk. Regulatory network. Network unknown. High-throughput experiments. Postgenomic. data. Machine learning. Statistics. Overview. Introduction Bayesian networks Comparative evaluation

1.32k views • 113 slides

Variational Bayes 101

Variational Bayes 101. The Bayes scene. Exact averaging in discrete/small models (Bayes networks) Approximate averaging: - Monte Carlo methods - Ensemble/mean field - Variational Bayes methods. Variational-Bayes .org MLpedia Wikipedia. ISP Bayes:

819 views • 48 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 calibration and uncertainty analysis of dynamic forest models

Bayesian calibration and uncertainty analysis of dynamic forest models. Marcel van Oijen CEH-Edinburgh. Input to forest models and output. Imperfect input data. Height. Environmental scenarios. NPP. Initial values. Soil C. Parameters. Model. Input to forest models and output. Model.

587 views • 42 slides

CS 679: Text Mining

This work is licensed under a Creative Commons Attribution-Share Alike 3.0 Unported License . CS 679: Text Mining. Lecture # 13: Gibbs Sampling for LDA. Credit: Many slides are from presentations by Tom Griffiths of Berkeley. Announcements. Required reading for Today

522 views • 41 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).

395 views • 39 slides

Dynamic Causal Modelling (DCM) for fMRI

Dynamic Causal Modelling (DCM) for fMRI. Andre Marreiros. Wellcome Trust Centre for Neuroimaging University College London. Overview. Dynamic Causal Modelling of fMRI. Definitions & motivation. The neuronal model (bilinear dynamics) The Haemodynamic model. Estimation: Bayesian framework.

602 views • 29 slides

DCM Advanced, Part II

DCM Advanced, Part II. Will Penny (Klaas Stephan) Wellcome Trust Centre for Neuroimaging Institute of Neurology University College London. SPM Course 2014 @ FIL. Overview. Extended DCM for fMRI: nonlinear, two-state, stochastic Embedding computational models in DCMs

838 views • 25 slides

DCM Advanced, Part II

DCM Advanced, Part II. Will Penny (Klaas Stephan) Wellcome Trust Centre for Neuroimaging Institute of Neurology University College London. SPM Course 2014 @ FIL. Overview. Extended DCM for fMRI: nonlinear, two-state, stochastic Embedding computational models in DCMs

366 views • 25 slides

Current Challenges in Bayesian Model Choice: Comments

Current Challenges in Bayesian Model Choice: Comments. William H. Jefferys Department of Astronomy University of Texas at Austin and Department of Statistics University of Vermont. The Problem. This paper thoroughly discusses modern Bayesian model choice, both theory and experience

331 views • 23 slides

Current Challenges in Bayesian Model Choice: Comments

Current Challenges in Bayesian Model Choice: Comments. William H. Jefferys Department of Astronomy University of Texas at Austin and Department of Statistics University of Vermont. The Problem. This paper thoroughly discusses modern Bayesian model choice, both theory and experience

234 views • 23 slides

Introducing model-data fusion to graduate students in ecology

Introducing model-data fusion to graduate students in ecology. Topics of discussion: The impact of NEON on ecology What are the desired outcomes from a basic curriculum? Content of a 1-2 semester course. manipulative observational. heterogeneity embraced. heterogeneity minimized.

235 views • 14 slides

COSMOLOGICAL BAYESIAN MODEL SELECTION

252 views • 12 slides