<--- Back to Details
First PageDocument Content
Reasoning / Inductive reasoning / Philosophy of science / Statistical inference / Critical thinking / Causality / Concept learning / Bayesian network / Inference / Bayesian inference / Theory / Argument
Date: 2015-03-24 11:58:24
Reasoning
Inductive reasoning
Philosophy of science
Statistical inference
Critical thinking
Causality
Concept learning
Bayesian network
Inference
Bayesian inference
Theory
Argument

Context-Sensitive Induction Patrick Shafto1 , Charles Kemp1 , Elizabeth Baraff1 , John D. Coley2 , & Joshua B. Tenenbaum1 1 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology 2

Add to Reading List

Source URL: ccdlab.rutgers.edu

Download Document from Source Website

File Size: 113,69 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