<--- Back to Details
First PageDocument Content
Regression analysis / Philosophy of science / Statistical methods / Statistical inference / Bayesian inference / Bayesian probability / Robust Bayes analysis / Bayesian network / Optimal design / Statistics / Bayesian statistics / Statistical theory
Date: 2014-01-16 13:18:33
Regression analysis
Philosophy of science
Statistical methods
Statistical inference
Bayesian inference
Bayesian probability
Robust Bayes analysis
Bayesian network
Optimal design
Statistics
Bayesian statistics
Statistical theory

Peter M¨ uller Department of Mathematics, U. of Texas Austin 1 University Station, C1200, Austin, Texas[removed]removed], http://www.math.utexas.edu/users/pmueller February 2013

Add to Reading List

Source URL: www.ma.utexas.edu

Download Document from Source Website

File Size: 181,92 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