Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Starting with a sample observed from a random variable X having a given distribution law with a set of non fixed parameters which we denote with a vector , a parametric inference problem consists of computing suitable values – call them estimates – of these parameters precisely on the basis of the sample. An estimate is suitable if replacing it with the unknown parameter does not cause major damage in next computations. In Algorithmic inference, suitability of an estimate reads in terms of compatibility with the observed sample.

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