Continuous-time markov duels: Theory and application
β Scribed by C. Bernard Barfoot
- Publisher
- John Wiley and Sons
- Year
- 1989
- Tongue
- English
- Weight
- 492 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0894-069X
No coin nor oath required. For personal study only.
β¦ Synopsis
This article extends the previous research on Markov duels (stochastic duels between weapons with Markov-dependent fire) to situations in which the time between rounds fired by each duelist is a continuous random variable that depends on the state of combat. Three starting conditions for the duels are considered: simultaneous detection, surprise by one duelist with continuous time detection by his opponent, and surprise with discrete time detection. The amount of surprise is treated as both a constant and a random variable. An application of these models to an evaluation of armored vehicles is described. The methods used to consider a variety of engagement ranges, tactical situations, and target types (both lethal and nonlethal) are discussed. The procedure for incorporating nonduel attrition into the analysis is described and the exchange rate (the expected number of enemy targets killed per armored vehicle killed) is derived.
π SIMILAR VOLUMES
We develop estimation methods for continuous-time Markov and semi-Markov non-homogeneous manpower systems using the notion of calendar time divided into 'time windows' by change points. The model parameters may only change at these change points but remain constant between them. Our estimation metho
Extending previous work of Ramlau-Hansen (Ramlau-Hansen, H., 1998a. Scand. Actuarial J., 143-156) for smooth Markov models, Hattendorff's theorem on the decomposition of the variance of the overall loss created by an insurance contract is generalized to policy developments given by an inhomogeneous
The examples in this second part of our paper illustrate the broad scope of the generalized Hattendorff theorem exposed in Part I as well as some limitations concerning the interpretability of numerical results derived from Hattendorff type theorems. In particular, they show that "mixed" situations