[HTML][HTML] Smoothed model checking for uncertain continuous-time Markov chains
We consider the problem of computing the satisfaction probability of a formula for stochastic
models with parametric uncertainty. We show that this satisfaction probability is a smooth …
models with parametric uncertainty. We show that this satisfaction probability is a smooth …
Moving from specifications to contracts in component-based design
SS Bauer, A David, R Hennicker… - … 2012, Held as Part of the …, 2012 - Springer
We study the relation between specifications of component behaviors and contracts
providing means to specify assumptions on environments as well as component guarantees …
providing means to specify assumptions on environments as well as component guarantees …
Approximate verification of the symbolic dynamics of Markov chains
A finite-state Markov chain M can be regarded as a linear transform operating on the set of
probability distributions over its node set. The iterative applications of M to an initial …
probability distributions over its node set. The iterative applications of M to an initial …
On the complexity of model checking interval-valued discrete time Markov chains
T Chen, T Han, M Kwiatkowska - Information Processing Letters, 2013 - Elsevier
We investigate the complexity of model checking (finite) interval-valued discrete time Markov
chains, that is, discrete time Markov chains where each transition is associated with an …
chains, that is, discrete time Markov chains where each transition is associated with an …
Parameter synthesis for parametric interval Markov chains
Abstract Interval Markov Chains (IMCs) are the base of a classic probabilistic specification
theory introduced by Larsen and Jonsson in 1991. They are also a popular abstraction for …
theory introduced by Larsen and Jonsson in 1991. They are also a popular abstraction for …
[HTML][HTML] Maximizing entropy over Markov processes
The channel capacity of a deterministic system with confidential data is an upper bound on
the amount of bits of data an attacker can learn from the system. We encode all possible …
the amount of bits of data an attacker can learn from the system. We encode all possible …
[HTML][HTML] Abstract probabilistic automata
Abstract Probabilistic Automata (PAs) are a widely-recognized mathematical framework for
the specification and analysis of systems with non-deterministic and stochastic behaviors …
the specification and analysis of systems with non-deterministic and stochastic behaviors …
Probabilistic programming process algebra
Formal modelling languages such as process algebras are widespread and effective tools in
computational modelling. However, handling data and uncertainty in a statistically …
computational modelling. However, handling data and uncertainty in a statistically …
Consistency for parametric interval Markov chains
B Delahaye - 2nd International Workshop on Synthesis of …, 2015 - drops.dagstuhl.de
Abstract Interval Markov Chains (IMCs) are the base of a classic probabilistic specification
theory by Larsen and Jonsson in 1991. They are also a popular abstraction for probabilistic …
theory by Larsen and Jonsson in 1991. They are also a popular abstraction for probabilistic …
Optimized smart sampling
M Parmentier, A Legay, F Chenoy - … on Bridging the Gap between AI and …, 2023 - Springer
We revisit the principle of Smart Sampling which makes it possible to apply Statistical Model
Checking on stochastic and non-deterministic systems. We point out difficulties in the design …
Checking on stochastic and non-deterministic systems. We point out difficulties in the design …