This paper presents a novel method for modeling the spatio-temporal movements of tourists at the macro level using Markov Chains methodology. Markov Chains are used extensively in modeling random phenomena which results in a sequence of events linked together under the assumption of first-order dependence. In this paper, we utilize Markov Chains to analyze the outcome and trend of events associ...

We consider the following problem: For a real-time probabilistic system S consisting of two submodules M1 and M2, the specification of the global system S is given, as well as part of the specification of M1 and part of the specification of M2 (the possible traces are known but not their probabilities nor their timing delays). We need to fully determine of M1 and M2 in a way to “best approximat...

We shall be concerned with the problem of determining quasista-tionary distributions for Markovian models directly from their transition rates Q. We shall present simple conditions for a-invariant measure m for Q to be-invariant for the transition function, so that if m is nite it can be normalized to produce a quasistationary distribution.

Markov chains are a useful tool for solving practical problems. In many real-life situations, we do not know the exact values of initial and transition probabilities; instead, we only know the intervals of possible values of these probabilities. Such interval-valued Markov chains were considered and analyzed by I. O. Kozine and L. V. Utkin in their Reliable Computing paper. In their paper, they...

We study the problem of model checking Interval-valued Discrete-time Markov Chains (IDTMC). IDTMCs are discrete-time finite Markov Chains for which the exact transition probabilities are not known. Instead in IDTMCs, each transition is associated with an interval in which the actual transition probability must lie. We consider two semantic interpretations for the uncertainty in the transition p...

We discuss conceptional and foundational aspects of Markov automata [22]. We place this model in the context of continuousand discrete-time Markov chains, probabilistic automata and interactive Markov chains, and provide insight into the parallel execution of such models. We further give a detailled account of the concept of relations on distributions, and discuss how this can generalise known ...

Among the class of discrete time Markovian processes, two models are widely used, the Markov chain and the Hidden Markov Model. A major di erence between these two models lies in the relation between successive outputs of the observed variable. In a visible Markov chain, these are directly correlated while in hidden models they are not. However, in some situations it is possible to observe both...

Özetçe —Bu çalışmada, Saklı Markov Modeli (SMM) olarak modellenen zaman serilerinin topaklandırılması için yeni bir yöntem önerilmektedir. Topaklardaki SMM parametrelerinin güncellenmesi için son yıllarda yapay öğrenme literatüründe popüler olmaya başlayan saklı değişken modelleri için spektral öğrenme yöntemleri kullanılmaktadır. Spektral yöntemler, alışılagelmiş beklenti-enbüyütme yaklaşımını...

In this paper, we obtain Markovian bounds on a function of a homogeneous discrete time Markov chain. For deriving such bounds, we use well known results on stochastic majorization of Markov chains and the Rogers-Pitman’s lumpability criterion. The proposed method of comparison between functions of Markov chains is not equivalent to generalized coupling method of Markov chains although we obtain...

we shall be concerned with the problem of determining quasi-stationary distributions for Markovian models directly from their transition rates Q. We shall present simple conditions for a p-invariant measure m for Q to be p-invariant for the transition function, so that if m is finite, it can be normalized to produce a quasi-stationary distribution. @ 2000 Elsevier Science Ltd. All rights reserv...