David A. Stanford

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We consider a stochastic fire growth model, with the aim of predicting the behaviour of large forest fires. Such a model can describe not only average growth, but also the variability of the growth. Implementing such a model in a computing environment allows one to obtain probability contour plots, burn size distributions, and distributions of time to(More)
The paper presents a recursive method of calculating ruin probabilities for non-Poisson claim processes, by looking at the surplus process embedded at claim instants. The developed method is exact. The processes considered have both claim sizes and the inter-claim revenue following selected phase type distributions. The numerical section contains figures(More)
For applications of stochastic fluid models, such as those related to wildfire spread and containment, one wants a fast method to compute time dependent probabilities. Erlangization is an approximation method that replaces various distributions at a time t by the corresponding ones at a random time with Erlang distribution having mean t . Here, we develop(More)
A number of nonlinear programming algorithms are proposed to obtain the approximate solutions for nonproduct form multiclass queueing network models, as well as priority queueing networks. Using sensitivity analysis, we develop an efficient iterative technique for closed queueing networks. We compare the approximate solutions obtained from our approach with(More)