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Statistical Methods for Fuzzy Data by Reinhard Viertl

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21

Bayesian regression analysis

In Bayesian inference the parameters θ are also considered as stochastic quantities with corresponding probability distribution π(·), called a priori distribution. In the case of continuous parameters the a priori distribution is determined by a probability density π(·) on the parameter space

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Then π(·) is a probability density on the parameter space, called a priori density.

The stochastic model for the dependent variable y is Yx ~ fx(·|θ).

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