<--- Back to Details
First PageDocument Content
Statistical inference / Statistical theory / Philosophy of science / Model selection / Bayesian probability / Economic model / Bayesian inference / Conceptual model / Akaike information criterion / Statistics / Science / Bayesian statistics
Statistical inference
Statistical theory
Philosophy of science
Model selection
Bayesian probability
Economic model
Bayesian inference
Conceptual model
Akaike information criterion
Statistics
Science
Bayesian statistics

Discussion of Session 3 Chaired byAndr6 Punt CSIRO Division of Marine Research GPO Box 1538, Hobart JASRecorded by Robin Thompson

Add to Reading List

Source URL: www.asfb.org.au

Download Document from Source Website

File Size: 251,63 KB

Share Document on Facebook

Similar Documents

Cataloging the Visible Universe through Bayesian Inference at Petascale Jeffrey Regier∗ , Kiran Pamnany† , Keno Fischer‡ , Andreas Noack§ , Maximilian Lam∗ , Jarrett Revels§ , Steve Howard¶ , Ryan Giordano¶ ,

Cataloging the Visible Universe through Bayesian Inference at Petascale Jeffrey Regier∗ , Kiran Pamnany† , Keno Fischer‡ , Andreas Noack§ , Maximilian Lam∗ , Jarrett Revels§ , Steve Howard¶ , Ryan Giordano¶ ,

DocID: 1xVn9 - View Document

Haptic SLAM: an ideal observer model for Bayesian inference of object shape and hand pose from contact dynamics Feryal M. P. Behbahani1 , Guillem Singla–Buxarrais2 and A. Aldo Faisal1,2,3 1

Haptic SLAM: an ideal observer model for Bayesian inference of object shape and hand pose from contact dynamics Feryal M. P. Behbahani1 , Guillem Singla–Buxarrais2 and A. Aldo Faisal1,2,3 1

DocID: 1xTqR - View Document

Improving the Identifiability of Neural Networks for Bayesian Inference Arya A. Pourzanjani∗, Richard M. Jiang∗, Linda R. Petzold Department of Computer Science University of California, Santa Barbara

Improving the Identifiability of Neural Networks for Bayesian Inference Arya A. Pourzanjani∗, Richard M. Jiang∗, Linda R. Petzold Department of Computer Science University of California, Santa Barbara

DocID: 1uZys - View Document

MAP estimate on GLMs  Stochastic Gradient Descent (SGD) MAP to Bayesian Inference

MAP estimate on GLMs Stochastic Gradient Descent (SGD) MAP to Bayesian Inference

DocID: 1uTAz - View Document

Bayesian Analysis, Number 4, pp. 817–846 Inference of global clusters from locally distributed data

Bayesian Analysis, Number 4, pp. 817–846 Inference of global clusters from locally distributed data

DocID: 1uGu0 - View Document