Paper - 2003
The majority of computational methods applied for the analysis of homogeneous Markov
reward models (MRMs) are not applicable for the analysis of inhomogeneous MRMs. By the
nature of inhomogeneous models, only forward differential equations can be used to describe
the model behaviour.
In this paper we provide forward partial differential equations describing the distribution
of reward measures of inhomogeneous MRMs. Based on this descriptions, we introduce the
set of ordinary differential equations that describes the behaviour of the moments of reward
measures when it is possible. This description of moments allows the effective numerical
analysis of rather large inhomogeneous MRMs.
A numerical example demonstrates the application of inhomogeneous MRMs in practice
and the numerical behaviour of the introduced analysis technique.
The majority of computational methods applied for the analysis of homogeneous Markov
reward models (MRMs) are not applicable for the analysis of inhomogeneous MRMs. By the
nature of inhomogeneous models, only forward differential equations can be used to describe
the model behaviour.
In this paper we provide forward partial differential equations describing the distribution
of reward measures of inhomogeneous MRMs. Based on this descriptions, we introduce the
set of ordinary differential equations that describes the behaviour of the moments of reward
measures when it is possible. This description of moments allows the effective numerical
analysis of rather large inhomogeneous MRMs.
A numerical example demonstrates the application of inhomogeneous MRMs in practice
and the numerical behaviour of the introduced analysis technique.