@MISC{Ramoni99bayesianmethods, author = {Marco Ramoni and Paola Sebastiani}, title = {Bayesian Methods}, year = {1999} }

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Abstract

Introduction Classical statistics provides methods to analyze data, from simple descriptive measures to complex and sophisticated models. The available data are processed and then conclusions about a hypothetical population --- of which the data available are supposed to be a representative sample --- are drawn. It is not hard to imagine situations, however, in which data are not the only available source of information about the population. Suppose, for example, we need to guess the outcome of an experiment that consists of tossing a coin. How many biased coins have we ever seen? Probably not many, and hence we are ready to believe that the coin is fair and that the outcome of the experiment can be either head or tail with the same probability. On the other hand, imagine that someone would tell us that the coin is forged so that it is more likely to land head. How can we take into account this information in the analysis of our data? This question becomes critical when we are consi