'Bayes factor' presentation slideshows

Four reasons to prefer Bayesian over orthodox statistics

636 views • 63 slides

DCM: Advanced Topics

DCM: Advanced Topics. Klaas Enno Stephan Translational Neuromodeling Unit (TNU ) Institute for Biomedical Engineering University of Zurich & Swiss Federal Institute of Technology (ETH) Zurich Wellcome Trust Centre for Neuroimaging Institute of Neurology University College London.

769 views • 60 slides

A Comparison of Energy Spectra in Different Parts of the Sky

A Comparison of Energy Spectra in Different Parts of the Sky. Carl Pfendner, Segev BenZvi, Stefan Westerhoff University of Wisconsin - Madison. Outline. Motivation New Statistical Method Tests of the Method Application to Pierre Auger Data Conclusions and Future. GZK Suppression.

616 views • 49 slides

Multimodal Brain Imaging

Multimodal Brain Imaging. Will D. Penny FIL, London. Guillaume Flandin, CEA, Paris Nelson Trujillo-Barreto, CNC, Havana. Neuronal Activity. Experimental Manipulation. Optical Imaging. MEG,EEG. PET. fMRI. FORWARD MODELS. Single/multi-unit recordings. Spatial convolution

610 views • 42 slides

Bayes Factors and inference

Bayes Factors and inference. Greg Francis. PSY 626: Bayesian Statistics for Psychological Science Fall 2018 Purdue University. Bayes Factor. The ratio of likelihood for the data under the null compared to the alternative Nothing special about the null, it compares any two models.

400 views • 39 slides

Bayesian statistical methods for HEP

Bayesian statistical methods for HEP. Terascale Statistics School DESY, Hamburg 1 October, 2008. Glen Cowan Physics Department Royal Holloway, University of London g.cowan@rhul.ac.uk www.pp.rhul.ac.uk/~cowan. Outline. Tuesday: The Bayesian method

461 views • 32 slides

Bayesian statistical methods for HEP

Bayesian statistical methods for HEP. Terascale Statistics School DESY, Hamburg 1 October, 2008. Glen Cowan Physics Department Royal Holloway, University of London g.cowan@rhul.ac.uk www.pp.rhul.ac.uk/~cowan. Outline. Tuesday: The Bayesian method

327 views • 32 slides

Dynamic Causal Modelling (DCM) for induced responses

Dynamic Causal Modelling (DCM) for induced responses. CC Chen Wellcome Trust Centre for Neuroimaging Institute of Neurology, UCL, London Dr. James Kilnear , Dr. Stefan Kiebel, Prof. Karl Friston and Dr. Nick Ward. Contents. Evoked and Induced activities

468 views • 31 slides

Bayesian Higgs combination based on event counts (follow-up from 11 May 07)

Bayesian Higgs combination based on event counts (follow-up from 11 May 07). ATLAS Statistics Forum CERN, 3 September, 2007. Glen Cowan Physics Department Royal Holloway, University of London g.cowan@rhul.ac.uk www.pp.rhul.ac.uk/~cowan. Outline.

328 views • 26 slides

For studying synchronization among brain regions

Dynamic Phase Coupling. For studying synchronization among brain regions Relate change of phase in one region to phase in others. Region 2. Region 1. ?. ?. Phase Interaction Function. Region 3. One Oscillator. Two Oscillators. Two Coupled Oscillators. 0.3. Different initial phases.

331 views • 26 slides

Recent Developments

Recent Developments. 15min. Previous Topics. https://exp-platform.com/2018StrataABTutorial/ Machine learning in Causal Inference Variance Reduction and Regression Adjustment Beyond Average Treatment Effect, a.k.a. Effect Heterogeneity. Topics. Bayes Factor Bounds

235 views • 23 slides

Analyzing Brain Connectivity for fMRI Data

From Localization to Connectivity and ... Lei Sheu 1/11/2011. Analyzing Brain Connectivity for fMRI Data. Background. Research interests: To study the association of human behaviors and brain functionality. Finding neural biomarkers of disease What do we know

895 views • 21 slides

UNED EUMETSAT

A new approach to the optimization of the extraction of astrometric and photometric information from multiwavelength images in cosmological fields. UNED EUMETSAT. Structure. Data Mining Needs : m ultiwavelength observations Objective 1: preliminary labelling

331 views • 21 slides

Using the Bayesian Information Criterion to Judge Models and Statistical Significance

568 views • 15 slides