Abstracts MLQA 2011 - Revision history

Ender at 10:56, 7 September 2011

2011-09-07T10:56:30Z

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	Finally we also list a few other techniques that apply to alternative models of continuous systems.		Finally we also list a few other techniques that apply to alternative models of continuous systems.

	==Small Thematic Working Group on Hidden Markov Models==		==Small Thematic Working Group on Hidden Markov Models ([http://wiki.ercim.eu/wg/MLQA/images/7/78/MLQA_2011-Nielson.pdf slides])==

	Flemming Nielson		Flemming Nielson

Ender at 13:56, 5 September 2011

2011-09-05T13:56:28Z

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	multi-stability and oscillation of stochastic models.		multi-stability and oscillation of stochastic models.

	==Approximation of continuous space systems and associated metrics and logics==		==Approximation of continuous space systems and associated metrics and logics ([http://wiki.ercim.eu/wg/MLQA/images/2/26/MLQA-2011-Desharnais.pdf slides])==

	Josée Desharnais		Josée Desharnais

Ender at 13:02, 5 September 2011

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	==Stochastic Hybrid Analysis of Markov Population Models==		==Stochastic Hybrid Analysis of Markov Population Models ([http://wiki.ercim.eu/wg/MLQA/images/2/2b/MLQA-2011-Wolf.pdf slides])==

	Verena Wolf		Verena Wolf

Ender at 13:01, 5 September 2011

2011-09-05T13:01:34Z

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	==Stochastic process algebras and ODEs==		==Stochastic process algebras and ODEs ([http://wiki.ercim.eu/wg/MLQA/images/7/79/MLQA-2011-Hillston.pdf slides])==
	Jane Hillston		Jane Hillston

Ender at 13:01, 5 September 2011

2011-09-05T13:01:11Z

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	==ODEs and discrete simulation==		==ODEs and discrete simulation ([http://wiki.ercim.eu/wg/MLQA/images/c/cc/MLQA-2011-Boudec.pdf slides])==
	Jean-Yves Le Boudec		Jean-Yves Le Boudec

Ender at 13:00, 5 September 2011

2011-09-05T13:00:36Z

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	set of ordinary differential equations, the so-called fluid approximation.		set of ordinary differential equations, the so-called fluid approximation.

	==On the Relationship between ODE, Simulation and Stochastic Process Algebras ([http://wiki.ercim.eu/wg/MLQA/~~index.php~~/~~Image:~~MLQA-2011-Bortolussi.pdf slides])==		==On the Relationship between ODE, Simulation and Stochastic Process Algebras ([http://wiki.ercim.eu/wg/MLQA/images/9/9a/MLQA-2011-Bortolussi.pdf slides])==
	Luca Bortolussi		Luca Bortolussi

Ender at 12:59, 5 September 2011

2011-09-05T12:59:00Z

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	set of ordinary differential equations, the so-called fluid approximation.		set of ordinary differential equations, the so-called fluid approximation.

	==On the Relationship between ODE, Simulation and Stochastic Process Algebras==		==On the Relationship between ODE, Simulation and Stochastic Process Algebras ([http://wiki.ercim.eu/wg/MLQA/index.php/Image:MLQA-2011-Bortolussi.pdf slides])==
	Luca Bortolussi		Luca Bortolussi

Ender at 10:30, 15 April 2011

2011-04-15T10:30:36Z

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 present techniques to reason about the mathematical foundations of
 multi-stability and oscillation of stochastic models.
 ==Small Thematic Working Group on Hidden Markov Models==

Ender at 09:27, 5 April 2011

2011-04-05T09:27:45Z

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	models described in stochastic process algebra and interpreted as a		models described in stochastic process algebra and interpreted as a
	set of ordinary differential equations, the so-called fluid approximation.		set of ordinary differential equations, the so-called fluid approximation.

			==On the Relationship between ODE, Simulation and Stochastic Process Algebras==
			Luca Bortolussi

			In this talk we will concentrate on the mathematical foundations of the fluid-approximation of stochastic process algebra models. We will discuss limit results that guarantee that, in the limit of large populations, the trajectories of the Continuous Time Markov Chain constructed from a Stochastic Process Algebra model cannot be distinguished from the solution of the fluid ODE. We will also discuss the conditions that must be satisfied by SPA models for such limit theorems to be applicable, commenting also on issues like error bounds and speed of convergence (which are connected with the properties of the phase space of ODE).
			Furthermore, we will present the relationships between the average behaviour of the SPA model and fluid ODE, which can be proved to be a first order approximation of the former, considering also approximations of higher-order moments.
			Finally, we will also discuss possible extensions of limit theorems to deal with situations in which the classic deterministic limit theorems are not applicable, for instance when not all the populations of agents can be assumed to increase to infinity. In these cases, the candidate limit process is no more an ODE, but rather a (stochastic) hybrid system.


	==Stochastic Hybrid Analysis of Markov Population Models==		==Stochastic Hybrid Analysis of Markov Population Models==

Ender at 14:31, 31 March 2011

2011-03-31T14:31:20Z

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	==ODEs and discrete simulation==		==ODEs and discrete simulation==
	~~J.Y.~~ Le Boudec		Jean-Yves Le Boudec

	We consider a generic model of N interacting objects, where each		We consider a generic model of N interacting objects, where each
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	==Stochastic Hybrid Analysis of Markov Population Models==		==Stochastic Hybrid Analysis of Markov Population Models==

	V. Wolf		Verena Wolf

	In this talk we will consider discrete-state Markov processes that		In this talk we will consider discrete-state Markov processes that