1. Goals and constraints of statistical inference
1.1 Formalism
Before setting out the elements needed to build a Bayesian inferential machine, we first consider the essential points that define statistical science. Its fundamental aim is to take observations of a random phenomenon and make an inference, i.e. a logical deduction about the probabilistic mechanism leading to this phenomenon. The ultimate aim of this methodology is to provide an analysis (or description) of a past phenomenon, or a forecast of a future phenomenon (of the same nature).
An example of statistical inference is supervised classification, where, using a sample of individuals or objects grouped into classes, a predictive model is built for any individual or object of the same type whose class is not...
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Goals and constraints of statistical inference
Fundamentals of Bayesian statistics
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