Behavioral and Brain Sciences 34 (4):203-204 (2011)

Abstract
The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models
Keywords No keywords specified (fix it)
Categories (categorize this paper)
DOI 10.1017/s0140525x1100032x
Options
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy


Upload a copy of this paper     Check publisher's policy     Papers currently archived: 53,666
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

From Covariation to Causation: A Causal Power Theory.Patricia W. Cheng - 1997 - Psychological Review 104 (2):367-405.
Theory-Based Causal Induction.Thomas L. Griffiths & Joshua B. Tenenbaum - 2009 - Psychological Review 116 (4):661-716.

View all 10 references / Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis.Matt Williams & Jon Williamson - 2006 - Journal of Logic, Language and Information 15 (1-2):155-178.

Analytics

Added to PP index
2013-10-27

Total views
24 ( #416,018 of 2,349,312 )

Recent downloads (6 months)
2 ( #330,956 of 2,349,312 )

How can I increase my downloads?

Downloads

My notes