|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.|
|Keywords||No keywords specified (fix it)|
|Through your library||Only published papers are available at libraries|
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.
Wolfgang Spohn (1994). On the Properties of Conditional Independence. In Paul Humphreys (ed.), Patrick Suppes, Scientific Philosopher Vol. 1: Probability and Probabilistic Causality. Kluwer.
Sorry, there are not enough data points to plot this chart.
Added to index2009-08-23
Total downloads10 ( #106,196 of 549,010 )
Recent downloads (6 months)0
How can I increase my downloads?