Statistical Models
نویسنده
چکیده
We have discussed graphical models. Graphical models are a formalism for representing families of probability distributions. They are connected to efficient algorithms for computing marginals, conditionals, and other properties of the family. We now turn to the central question: How can we use joint probability distributions to make meaningful statements about observed data? (A) In a statistical model, the data are represented as observed random variables a joint distribution that also involves hidden random variables. We calculate the conditional distribution of the hidden variables given the observed variables. This conditional tells us about the data and helps form predictions. (B) In a statistical model, the data are represented as shaded nodes in a graph-ical model that also involves unshaded nodes. We perform probabilistic inference of the unshaded nodes. This inference tells us about the data and helps form predictions. Observed: nucleotides; Hidden: whether they are associated with genes.
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