Catching Up Faster in Bayesian Model Selection and Model Averaging (T61) Peter Gru¨ nwald Tim van Erven 1. Introduction - Factor theory - Document - PDFSEARCH.IO

First Page		Document Content
Date: 2012-06-05 11:28:52 Statistical inference Bayesian inference Markov models Markov chain Akaike information criterion Bayes factor Estimation theory Graphical model Expectation–maximization algorithm Statistics Bayesian statistics Model selection		Catching Up Faster in Bayesian Model Selection and Model Averaging (T61) Peter Gru¨ nwald Tim van Erven 1. Introduction & Summary Add to Reading List Source URL: www.timvanerven.nl Download Document from Source Website File Size: 101,15 KB Share Document on Facebook

	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 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 DocID: 1uZys - View Document
	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 DocID: 1uGu0 - View Document