Probabilistic Networks: New Models and New Methods
نویسنده
چکیده
In this paper I describe the implementation of a probabilistic regression model in BUGS. BUGS is a program that carries out Bayesian inference on statistical problems using a simulation technique known as Gibbs sampling. It is possible to implement surprisingly complex regression models in this environment. I demonstrate the simultaneous inference of an interpolant and an input-dependent noise level. 1 Traditional regression models and their Bayesian interpretation In traditional regression methods, the interpolant y(x) is represented as a parameterized function y(x; w), and, given data fx n ; t n g N n=1 , the parameters w are optimized to minimize the weighted
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