Markovian Process Algebras approximate their model of synchronisation events in order to preserve their Markovian nature. This paper investigates synchronisation models in a stochastic context and focuses on how the Markovian approximation of synchronisation a ects the accuracy of the performance model. TIPP and PEPA are used as speci c cases throughout, and their di erent methods of synchronis...

Markovian Process Algebras approximate their model of synchronisation events in order to preserve their Markovian nature. This paper investigates synchronisation models in a stochastic context and focuses on how the Markovian approximation of synchronisation a ects the accuracy of the performance model. TIPP and PEPA are used as speci c cases throughout, and their di erent methods of synchronis...

Markovian arrival processes are a powerful class of stochastic processes to represent stochastic workloads that include autocorrelation in performance or dependability modeling. However, fitting the parameters of a Markovian arrival process to given measurement data is non-trivial and most known methods focus on a single class case, where all events are of the same type and only the sequence of...

Generic non-Markovian quantum processes have infinitely long memory, implying an exact description that grows exponentially in complexity with observation time. Here, we present a finite memory ansatz approximates (or recovers) the true process errors bounded by strength of memory. The introduced is operational quantity and depends on way probed. Remarkably, recovery error smallest over all pos...

Most noise-characterization methods for quantum technologies assume Markovianity, meaning that the environment and system have no memory of their interactions with each other, because it is inefficient computationally demanding to take temporal correlations into account. Here, authors propose a more efficient machine learning method estimating non-Markovian noise implement in proof-of-principle...