Bayesian Inference of Noise Levels in Regression

  title={Bayesian Inference of Noise Levels in Regression},
  author={Christopher M. Bishop and Cazhaow S. Quazaz},
In most treatments of the regression problem it is assumed that the distribution of target data can be described by a deterministic function of the inputs, together with additive Gaussian noise having constant variance. The use of maximum likelihood to train such models then corresponds to the minimization of a sum-of-squares error function. In many applications a more realistic model would allow the noise variance itself to depend on the input variables. However, the use of maximum likelihood… CONTINUE READING