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1 Some Modern Applications of Graphical Models Emory University Form

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we're going to continue looking at multivariate probabilistic models and specifically we're going to look next at probabilistic graphical models uh so this is a topic we're going to look at a lot in this course probabilistic graphical models are a really powerful way of representing uh relationships and uncertainty and uh probabilistic distributions over collections of random variables uh and in particular let's look at a bayes bayesian network uh let's look at a let's start with a specific example which is the following network of random variables let's look at these four random variables cloudy sprinkler rain wet grass these are all binary random variables so cloudy is it cloudy or not uh is did the sprinkler run did it rain and is the grass wet um so we uh define a bayesian network a bayesian network is a way of representing a conditional dependence and conditional independence among random variables so we're going to draw a arrow from one variable to another if there is a condition

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