The Random Effects in Multilevel Models: Getting Them Wrong and Getting Them Right

Biologically-Inspired Design: Getting It Wrong and Getting It Right

Large, complex computing systems have many similarities to biological systems, at least at a high level. They consist of a very large number of components, the interactions between which are complex and dynamic, and the overall behavior of the system is not always predictable even if the components are well understood. These similarities have led the computing community to look to biology for d...

ROC curves and confidence intervals : getting them

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Propagation Models and Fitting Them for the Boolean Random Sets

In order to study the relationship between random Boolean sets and some explanatory variables, this paper introduces a Propagation model. This model can be applied when corresponding Poisson process of the Boolean model is related to explanatory variables and the random grains are not affected by these variables. An approximation for the likelihood is used to find pseudo-maximum likelihood esti...

Getting it right when things go wrong.

Requirements Engineering - Getting Right from Wrong

The neats: proponents of formal-based, knowledge-intensive smart systems to revolutionize the requirements process. The eventual success of the neat approach is less certain. There are several opinions of the answer to: ‘Can the neats ever playa significant role in requirements engineering?’: a. Never – the extra effort of doing things the neat way will always exceed the payoff. b. Hardly ever ...