1st-September-2020
• The notion of conditional independence can be used to give a concise representation of many domains. The idea is that, given a random variable X, a small set of variables may exist that directly affect the variable's value in the sense that X is conditionally independent of other variables given values for the directly affecting variables. The set of locally affecting variables is called the Markov blanket. This locality is what is exploited in a belief network. A belief network is a directed model of conditional dependence among a set of random variables. The precise statement of conditional independence in a belief network takes into account the directionality.
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