29,225 research outputs found
Quantitative Approximation of the Probability Distribution of a Markov Process by Formal Abstractions
The goal of this work is to formally abstract a Markov process evolving in
discrete time over a general state space as a finite-state Markov chain, with
the objective of precisely approximating its state probability distribution in
time, which allows for its approximate, faster computation by that of the
Markov chain. The approach is based on formal abstractions and employs an
arbitrary finite partition of the state space of the Markov process, and the
computation of average transition probabilities between partition sets. The
abstraction technique is formal, in that it comes with guarantees on the
introduced approximation that depend on the diameters of the partitions: as
such, they can be tuned at will. Further in the case of Markov processes with
unbounded state spaces, a procedure for precisely truncating the state space
within a compact set is provided, together with an error bound that depends on
the asymptotic properties of the transition kernel of the original process. The
overall abstraction algorithm, which practically hinges on piecewise constant
approximations of the density functions of the Markov process, is extended to
higher-order function approximations: these can lead to improved error bounds
and associated lower computational requirements. The approach is practically
tested to compute probabilistic invariance of the Markov process under study,
and is compared to a known alternative approach from the literature.Comment: 29 pages, Journal of Logical Methods in Computer Scienc
Enhancing Approximations for Regular Reachability Analysis
This paper introduces two mechanisms for computing over-approximations of
sets of reachable states, with the aim of ensuring termination of state-space
exploration. The first mechanism consists in over-approximating the automata
representing reachable sets by merging some of their states with respect to
simple syntactic criteria, or a combination of such criteria. The second
approximation mechanism consists in manipulating an auxiliary automaton when
applying a transducer representing the transition relation to an automaton
encoding the initial states. In addition, for the second mechanism we propose a
new approach to refine the approximations depending on a property of interest.
The proposals are evaluated on examples of mutual exclusion protocols
Generalization Strategies for the Verification of Infinite State Systems
We present a method for the automated verification of temporal properties of
infinite state systems. Our verification method is based on the specialization
of constraint logic programs (CLP) and works in two phases: (1) in the first
phase, a CLP specification of an infinite state system is specialized with
respect to the initial state of the system and the temporal property to be
verified, and (2) in the second phase, the specialized program is evaluated by
using a bottom-up strategy. The effectiveness of the method strongly depends on
the generalization strategy which is applied during the program specialization
phase. We consider several generalization strategies obtained by combining
techniques already known in the field of program analysis and program
transformation, and we also introduce some new strategies. Then, through many
verification experiments, we evaluate the effectiveness of the generalization
strategies we have considered. Finally, we compare the implementation of our
specialization-based verification method to other constraint-based model
checking tools. The experimental results show that our method is competitive
with the methods used by those other tools. To appear in Theory and Practice of
Logic Programming (TPLP).Comment: 24 pages, 2 figures, 5 table
Model Checking Probabilistic Pushdown Automata
We consider the model checking problem for probabilistic pushdown automata
(pPDA) and properties expressible in various probabilistic logics. We start
with properties that can be formulated as instances of a generalized random
walk problem. We prove that both qualitative and quantitative model checking
for this class of properties and pPDA is decidable. Then we show that model
checking for the qualitative fragment of the logic PCTL and pPDA is also
decidable. Moreover, we develop an error-tolerant model checking algorithm for
PCTL and the subclass of stateless pPDA. Finally, we consider the class of
omega-regular properties and show that both qualitative and quantitative model
checking for pPDA is decidable
Palm distributions for log Gaussian Cox processes
This paper establishes a remarkable result regarding Palmdistributions for a
log Gaussian Cox process: the reduced Palmdistribution for a log Gaussian Cox
process is itself a log Gaussian Coxprocess which only differs from the
original log Gaussian Cox processin the intensity function. This new result is
used to study functionalsummaries for log Gaussian Cox processes
Approximate probabilistic verification of hybrid systems
Hybrid systems whose mode dynamics are governed by non-linear ordinary
differential equations (ODEs) are often a natural model for biological
processes. However such models are difficult to analyze. To address this, we
develop a probabilistic analysis method by approximating the mode transitions
as stochastic events. We assume that the probability of making a mode
transition is proportional to the measure of the set of pairs of time points
and value states at which the mode transition is enabled. To ensure a sound
mathematical basis, we impose a natural continuity property on the non-linear
ODEs. We also assume that the states of the system are observed at discrete
time points but that the mode transitions may take place at any time between
two successive discrete time points. This leads to a discrete time Markov chain
as a probabilistic approximation of the hybrid system. We then show that for
BLTL (bounded linear time temporal logic) specifications the hybrid system
meets a specification iff its Markov chain approximation meets the same
specification with probability . Based on this, we formulate a sequential
hypothesis testing procedure for verifying -approximately- that the Markov
chain meets a BLTL specification with high probability. Our case studies on
cardiac cell dynamics and the circadian rhythm indicate that our scheme can be
applied in a number of realistic settings
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