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Bayesian updating

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Once fully specified, a Bayesian network compactly represents the joint probability distribution (JPD) and, thus, can be used for computing the posterior probabilities of any subset of variables given evidence about any other subset.To learn more, please see Chapter 2 in our book, Bayesian Networks & Bayesia Lab.Probabilistic models based on directed acyclic graphs (DAG) have a long and rich tradition, beginning with the work of geneticist Sewall Wright in the 1920s. Within statistics, such models are known as directed graphical models; within cognitive science and artificial intelligence, such models are known as Bayesian networks. Thomas Bayes (1702-1761), whose rule for updating probabilities in the light of new evidence is the foundation of the approach. Bayes addressed both the case of discrete probability distributions of data and the more complicated case of continuous probability distributions.In the discrete case, Bayes’ theorem relates the conditional and marginal probabilities of events .

Advanced Bayesian filters can examine multiple words in a row, as another data point.Before updating any information on the Bayes rule, Bayesian network first evaluates the nature and reliability of the new data.Bayesian networks are usable in fields where there is need for prediction and outcome is uncertain.It’s a bit like a weighted average, and helps us compare against the overall chance of a positive result.In our case, Pr(X) gets really large because of the potential for false positives.As we analyze the words in a message, we can compute the chance it is spam (rather than making a yes/no decision).