David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
This paper examines deﬁnitions of independence for events and variables in the context of full conditional measures; that is, when conditional probability is a primitive notion and conditioning is allowed on null events. Several independence concepts are evaluated with respect to graphoid properties; we show that properties of weak union, contraction and intersection may fail when null events are present. We propose a concept of “full” independence, characterize the form of a full conditional measure under full independence, and suggest how to build a theory of Bayesian networks that accommodates null events.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
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
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Wolfgang Spohn (1980). Stochastic Independence, Causal Independence, and Shieldability. Journal of Philosophical Logic 9 (1):73 - 99.
Luc Bovens & EJ Olsson (2000). Coherentism, Reliability and Bayesian Networks. Mind 109 (436):685-719.
Christopher S. I. Mccurdy (1996). Humphrey's Paradox and the Interpretation of Inverse Conditional Propensities. Synthese 108 (1):105 - 125.
Gregory Wheeler (2009). Focused Correlation and Confirmation. British Journal for the Philosophy of Science 60 (1):79-100.
Added to index2009-08-23
Total downloads10 ( #141,181 of 1,096,624 )
Recent downloads (6 months)0
How can I increase my downloads?