DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails Martin Atzmueller and Andreas Schmidt and Mark Kibanov University of Kassel, Research Center for Information Syst

First Page		Document Content
Date: 2017-10-04 00:45:19 Design of experiments Statistics Probability and statistics Markov models Statistical theory Psychometrics Statistical hypothesis testing Graph theory Markov chain Bayesian inference Matrix Data transformation		DASHTrails: An Approach for Modeling and Analysis of Distribution-Adapted Sequential Hypotheses and Trails Martin Atzmueller and Andreas Schmidt and Mark Kibanov University of Kassel, Research Center for Information Syst Add to Reading List Source URL: gdac.uqam.ca Download Document from Source Website File Size: 1,53 MB 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