Model checking Markov reward models unites two different approaches of model-based system validation. On the one hand, Markov reward models have a long tradition in model-based performance and dependability evaluation. On the other hand, a formal method like model checking allows for the precise specification and verification of complex qualitative system properties. The logic CSRL (an extensio...

due to the effective role of markov models in customer relationship management (crm), there is a lack of comprehensive literature review which contains all related literatures. in this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. one hundred articles were identified and reviewed to find direct relevance for applying markov models...

With the increasing complexity of multiprocessor and distributed processing systems, the need to develop efficient and accurate modeling methods is evident. Fault tolerance and degradable performance of such systems has given rise to considerable interest in models for the combined evaluation of performance and reliability [l], [2]. Markov or semi-Markov reward models can be used to evaluate th...

Markov-reward models, as extensions of continuous-time Markov chains, have received increased attention for the specification and evaluation of performance and dependability properties of systems. Until now, however, the specification of reward-based performance and dependability measures has been done manually and informally. In this paper, we change this undesirable situation by the introduct...

Multivariate reward processes with reward functions of constant rates, defined on a semi-Markov process, first were studied by Masuda and Sumita, 1991. Reward processes with nonlinear reward functions were introduced in Soltani, 1996. In this work we study a multivariate process , , where are reward processes with nonlinear reward functions respectively. The Laplace transform of the covar...

We analyze the moments of the accumulated reward over the interval (0, t) in a continuous-time Markov chain. We develop a numerical procedure to efficiently compute the normalized moments using the uniformization technique. Our algorithm involves auxiliary quantities whose convergence is analyzed, and for which we provide a probabilistic interpretation. Key-words: Markov models, accumulated rew...

Probabilistic model checking mainly concentrates on techniques for reasoning about the probabilities of certain path properties or expected values of certain random variables. For the quantitative system analysis, however, there is also another type of interesting performance measure, namely quantiles. A typical quantile query takes as input a lower probability bound p ∈ ]0, 1] and a reachabili...

First analysis of Markov Reward Models (MRM) resulted in a double transform expression, whose numerical solution is based on the inverse transformations both in time and reward variable domain. Better numerical methods were proposed based on the time domain properties of these models, such as the set of partial differential equation describes the process evolution in time. This paper introduces...

This paper describes some models and measures of reliability for multistate systems. The expected cumulative reward for the continuous time Markov reward models are used for deriving the structure function for a multistate system where the system and its components can have different performance levels ranging from perfect functioning to complete failure. The suggested approach presents with re...