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- Luca Bortolussi, Jane Hillston, Diego Latella, Mieke Massink
- Perform. Eval.
- 2013

In this paper we present an overview of the field of deterministic approximation of Markov processes, both in discrete and continuous time. We will discuss mean field approximation of discrete time Markov chains and fluid approximation of continuous time Markov chains, considering the cases in which the deterministic limit process lives in continuous time… (More)

- Luca Bortolussi, Jane Hillston
- CONCUR
- 2012

In this paper we investigate a potential use of fluid approximation techniques in the context of stochastic model checking of CSL formulae. We focus on properties describing the behaviour of a single agent in a (large) population of agents, exploiting a limit result known also as fast simulation. In particular, we will approximate the behaviour of a single… (More)

- Luca Bortolussi, Alberto Policriti
- Electr. Notes Theor. Comput. Sci.
- 2007

We tackle the problem of relating models of systems (mainly biological systems) based on stochastic process algebras (SPA) with models based on differential equations. We define a syntactic procedure that translates programs written in stochastic Concurrent Constraint Programming (sCCP) into a set of Ordinary Differential Equations (ODE), and also the… (More)

- Federico Fogolari, Lidia Pieri, +5 authors Paolo Viglino
- BMC Structural Biology
- 2006

Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an… (More)

Stochastic models such as Continuous-Time Markov Chains (CTMC) and Stochastic Hybrid Au-tomata (SHA) are powerful formalisms to model and to reason about the dynamics of biological systems, due to their ability to capture the stochasticity inherent in biological processes. A classical question in formal modelling with clear relevance to biological modelling… (More)

- Ezio Bartocci, Luca Bortolussi, Guido Sanguinetti
- FORMATS
- 2014

We present a novel approach to learn logical formulae characterising the emergent behaviour of a dynamical system from system observations. At a high level, the approach starts by devising a data-driven statistical abstraction of the system. We then propose general optimisation strategies for selecting formu-lae with high satisfaction probability, either… (More)

We explore the possibility of designing a constraint-based algorithm for constructing subsets of DNA words satisfying given constraints (the so-called DNA word design problem). In this direction, we use symbolic representation of sets of strings, and define a propagation algorithm on these representations. Some preliminary results are presented , together… (More)

- Luca Bortolussi, Alberto Policriti
- J. Log. Comput.
- 2013

We define a semantics for stochastic Concurrent Constraint Programming (sCCP), a stochastic process algebra, in terms of stochas-tic hybrid automata with piecewise deterministic continuous dynamics. To each program we associate a lattice of hybrid models, parameter-ized with respect to the degree of discreteness left. We study some properties of this… (More)

- Ezio Bartocci, Luca Bortolussi, Guido Sanguinetti
- ArXiv
- 2013

We present a novel approach to learn the formulae characterising the emergent behaviour of a dynamical system from system observations. At a high level, the approach starts by devising a statistical dynamical model of the system which optimally fits the observations. We then propose general optimisation strategies for selecting high support formulae (under… (More)

- Luca Bortolussi, Nicolas Gast
- 2016 46th Annual IEEE/IFIP International…
- 2016

We consider stochastic models in presence of uncertainty, originating from lack of knowledge of parameters or by unpredictable effects of the environment. We focus on population processes, encompassing a large class of systems, from queueing networks to epidemic spreading. We set up a formal framework for imprecise stochastic processes, where some… (More)