POPULATION GROWTH--IS ENOUGH, ENOUGH?

Labor and Human Capital,

When is Enough Good Enough in Gravitational Wave Source Modeling?

A typical approach to developing an analysis algorithm for analyzing gravitational wave data is to assume a particular waveform and use its characteristics to formulate a detection criteria. Once a detection has been made, the algorithm uses those same characteristics to tease out parameter estimates from a given data set. While an obvious starting point, such an approach is initiated by assuming a single, correct model for the waveform regardless of the signal strength, observation length, noise, etc. This paper introduces the method of Bayesian model selection as a way to select the most plausible waveform model from a set of models given the data and prior information. The discussion is done in the scientific context for the proposed Laser Interferometer Space Antenna.Comment: 7 pages, 2 figures, proceedings paper for the Sixth International LISA Symposiu

Management of the Expanded Public Works Programme in the Department of Public Works : KwaZulu-Natal Province.

Doctor of Public Administration. University of KwaZulu-Natal, Westville 2014.No abstract available.1. Preliminary pages (except title page), is missing from the digital copy. 2. Pages ii-xxii and Annexures is missing from the digital copy

Optimal enough?

An alleged weakness of heuristic optimisation methods is the stochastic character of their solutions. That is, instead of finding a truly optimal solution, they only provide a stochastic approximation of this optimum. In this paper we look into a particular application, portfolio optimisation. We demonstrate two points: firstly, the randomness of the ‘optimal’ solution obtained from the algorithm can be made so small that for all practical purposes it can be neglected. Secondly, and more importantly, we show that the remaining randomness is swamped by the uncertainty coming from the data. In particular, we show that as a result of the bad conditioning of the problem, minor changes in the solution lead to economically meaningful changes in the solution’s out-of-sample performance. The relationship between in-sample fit and out-of-sample performance is not monotonous, but still, we observe that up to a point better solutions in-sample lead to better solutions out-of-sample. Beyond this point, however, there is practically no more cause for improving the solution any further, since any improvement will only lead to unpredictable changes (noise) out-of-sample.Optimisation heuristics, Portfolio Optimisation, Threshold Accepting