Probabilistic models explicitly take into account uncertainty and deal with our imperfect knowledge of the world. Suchmodels are of fundamental significance in Machine Learning since our understanding of the world will always be limited by our observations and understanding. We will focus initially on using probabilistic models as a kind of expert system.
In Part I, we assume that the model is fully specified. That is, given a model of the environment, how can we use it to answer questions of interest? We will relate the complexity of inferring quantities of interest to the structure of the graph describing the model. In addition, we will describe operations in terms of manipulations on the corresponding graphs. As we will see, provided the graphs are simple tree-like structures, most quantities of interest can be computed efficiently.
Part I deals with manipulating mainly discrete variable distributions and forms the background to all the later material in the book.
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