State-based analysis of discrete event systems notoriously suffers from the largeness of
state spaces, which often grow exponentially with the size of the model. Since non-trivial …

The complexity of stochastic models of real-world systems is usually managed by
abstracting details and structuring models in a hierarchical manner. Systems are often built …

The fundamental need for models in every field of design stems from the absolute and
essential necessity for complexity domination. The prediction and verification competence is …

Despite the development of many modeling formalisms and model solution methods, most
tool implementations support only a single formalism. Furthermore, models expressed in a …

A key advantage of the Möbius modeling environment is the ease with which one can
incorporate new modeling formalisms, model composition and connection methods, and …

Large and complex models can be difficult to analyze using static analysis results from
current tools, including the Möbius modeling framework, which provides a powerful …

Möbius has found numerous applications in computational biology to build and solve
stochastic models of biological processes. It provides the user with a modeling workflow and …

Markov reward models considered in this thesis are compactly described by means of
Markovian extensions of well-known high-level model description formalisms. For …

Abstract Continuous time Markov chains (CTMCs) are among the most fundamental
mathematical structures used for performance and dependability modeling of …

Multiprocessor architectures with few but powerful processors are gaining more and more
popularity. We describe a parallel iterative algorithm to perform randomization for a …