In this paper we extend the predicate logic introduced in [BRS02] in order to deal with Semi-Markov Processes. We prove that with respect to qualitative probabilistic properties, model checking is decidable for this logic applied to Semi-Markov Processes. Furthermore we apply our logic to Probabilistic Timed Automata considering classical and urgent semantics, and considering also predicates on...

Since the advent of Markovian Process Algebras, users have requested the ability to employ a greater variety of action distribution. We present a conservative extension to the popular Markovian process algebra, PEPA, which incorporates generally distributed sojourn-times for action duration. Just as a PEPA model generates a Markov chain for analysis purposes, so semi-Markov PEPA produces a semi...

We study the threshold dynamics of a stochastic SAIRS-type model with vaccination, where role asymptomatic and symptomatic infectious individuals is explicitly considered in epidemic dynamics. In model, values disease transmission rate may switch between different levels under effect semi-Markov process. provide sufficient conditions ensuring almost surely extinction persistence time mean. case...

The hidden Markov chain (HMC) model is a couple of random sequences (X,Y), in which X is an unobservable Markov chain, and Y is its observable ‘‘noisy version’’. The chain X is a Markov one and the components of Y are independent conditionally on X. Such a model can be extended in two directions: (i) X is a semi-Markov chain and (ii) the distribution of Y conditionally on X is a ‘‘long dependen...

Markov order-1 conditional random fields (CRFs) and semi-Markov CRFs are two popular models for sequence segmentation and labeling. Both models have advantages in terms of the type of features they most naturally represent. We propose a hybrid model that is capable of representing both types of features, and describe efficient algorithms for its training and inference. We demonstrate that our h...