<--- 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

Unfolding Crime Scenarios with Variations: A Method for Building a Bayesian Network for Legal Narratives Charlotte S. VLEK a,1 , Henry PRAKKEN b,c , Silja RENOOIJ b and Bart VERHEIJ a,d a Institute of Artificial Intellig

Unfolding Crime Scenarios with Variations: A Method for Building a Bayesian Network for Legal Narratives Charlotte S. VLEK a,1 , Henry PRAKKEN b,c , Silja RENOOIJ b and Bart VERHEIJ a,d a Institute of Artificial Intellig

DocID: 1uDKU - View Document

Network Theory III: Bayesian Networks, Information and Entropy John Baez, Brendan Fong, Tobias Fritz, Tom Leinster Given finite sets X and Y , a stochastic map f : X Y assigns a

Network Theory III: Bayesian Networks, Information and Entropy John Baez, Brendan Fong, Tobias Fritz, Tom Leinster Given finite sets X and Y , a stochastic map f : X Y assigns a

DocID: 1umlL - View Document

From Arguments to Constraints on a Bayesian Network a Floris BEX a , Silja RENOOIJ a Information and Computing Sciences, Utrecht University, The Netherlands

From Arguments to Constraints on a Bayesian Network a Floris BEX a , Silja RENOOIJ a Information and Computing Sciences, Utrecht University, The Netherlands

DocID: 1tDUV - View Document

3.3. Independencies in Graphs  Algorithm 3.1 Algorithm for finding nodes reachable from X given Z via active trails Procedure Reachable ( G, // Bayesian network graph X, // Source variable

3.3. Independencies in Graphs Algorithm 3.1 Algorithm for finding nodes reachable from X given Z via active trails Procedure Reachable ( G, // Bayesian network graph X, // Source variable

DocID: 1tiRB - View Document

Bayesian Network Automata for Modelling Unbounded Structures James Henderson Department of Computer Science University of Geneva Geneva, Switzerland

Bayesian Network Automata for Modelling Unbounded Structures James Henderson Department of Computer Science University of Geneva Geneva, Switzerland

DocID: 1t04K - View Document