We present a neural net algorithm for parameter estimation in the context of large cosmological data sets. Cosmological data sets present a particular challenge to pattern-recognition algorithms since the input patterns (galaxy redshift surveys, maps of cosmic microwave background anisotropy) are not fixed templates overlaid with random noise, but rather are random realizations whose informatio...

In this paper we consider the class of stochastic discrete optimization problems in which the feasibility of a solution does not depend on the particular values the random elements in the problem take. Given a regret function, we introduce the concept of the risk associated with a solution, and define an optimal solution as one having the least possible risk. We show that for discrete optimizat...

We present a neural net algorithm for parameter estimation in the context of large cosmological data sets. Cosmological data sets present a particular challenge to patternrecognition algorithms since the input patterns (galaxy redshift surveys, maps of cosmic microwave background anisotropy) are not fixed templates overlaid with random noise, but rather are random realizations whose information...

A retrospective evaluation of attitudinal, behavioural and knowledge change among diverse stakeholder groups was conducted in Limpopo Province of South Africa to assess the effectiveness of a series of values clarification (VC) interventions. Telephone and face-to-face interview (193) results revealed that over two-thirds (70.2%) reported behavioural changes and 93.2% reported increased compass...