Independence for full conditional measures, graphoids and bayesian networks
| Abstract | 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. | |||||||||
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Christopher S. I. Mccurdy (1996). Humphrey's Paradox and the Interpretation of Inverse Conditional Propensities. Synthese 108 (1):105 - 125.
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