Regression with input-dependent noise: A Bayesian treatment

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 to train such models would give highly biased results. In this paper we show how a Bayesian treatment can allow for an input-dependent variance while overcoming the bias of maximum likelihood.

An exploration of policies and practices used to showcase production facilities

Manufacturers who seek innovative ways in which to differentiate their products and services should not overlook the value of showcasing their production facilities. By careful design, visitors can be exposed to a series of experiences that can help to emphasize the value built into products. This topic has, however, received almost no attention by manufacturing researchers. Therefore, this paper describes a study of six manufacturers and, from this, proposes a set of guidelines for showcasing production facilities. Although exploratory, this work provides both a guide to manufacturers and a platform for more in-depth research. The guidelines and the case studies on which they are based are all described within the paper.