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- Den Boychuk, W. John Braun, Reg J. Kulperger, Zinovi L. Krougly, David A. Stanford
- Environmental and Ecological Statistics
- 2007

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)

Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given… (More)

- Zinovi L. Krougly, Irena F. Creed, David A. Stanford
- Computers & Geosciences
- 2009

A stochastic model for generating disturbances in landscapes that interfaces with geographic information systems (GIS) is presented. The model operates on a lattice (rectangular array of points) using a space–time Markov process, which gives a stochastic simulation of growth patterns in terms of parameters of the local region. The model generates… (More)

- Zinovi L. Krougly, David J. Jeffrey, Dina Tsarapkina
- 2013 15th International Symposium on Symbolic and…
- 2013

We introduce a Matlab mprec arbitrary precision library with applications to numerical analysis. For maximum efficiency arithmetic operators and algebraic functions are implemented in the mpreal class. The examples are chosen to reflect the diversity of types of problems for which multiple precision can play a useful role.

- Zinovi L. Krougly, David A. Stanford
- Perform. Eval.
- 2005

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)

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