In this thesis we study the mixing time of systematic scan Markov chains on finite spin systems. A systematic scan Markov chain is a Markov chain which updates the sites in a deterministic order and this type of Markov chain is often seen as intuitively appealing in terms of implementation to scientists conducting experimental work. Until recently systematic scan Markov chains have largely resi...

the notion of infinite order markov process is introduced and the markov property of the flow of information is established.

investors use different approaches to select optimal portfolio. so, optimal investment choices according to return can be interpreted in different models. the traditional approach to allocate portfolio selection called a mean - variance explains. another approach is markov chain. markov chain is a random process without memory. this means that the conditional probability distribution of the nex...

and Conclusions The technique introduced in this paper is a new technique for analyzing fault tolerant designs under considerable uncertainty, such as seen in unique or few-of-a-kind devices in poorly known environments or pre-prototype design analyses. This technique is able to provide useful information while maintaining the uncertainty inherent in the original speciications. The technique in...

analyzing the process of land use and cover changes during long periods of time and predicting the future changes is highly important and useful for the land use managers. in this study, the land use maps in the ardabil plain in north-west part of iran for four periods (1989, 1998, 2009 and 2013) are extracted and analyzed through remote sensing technique, using the land-sat satellite images. t...

A solution method for solving Markov chains for a class of stochastic process algebra terms is presented. The solution technique is based on a reformulation of the underlying continuous-time Markov chain (CTMC) in terms of semi-Markov processes. For the reformulation only local information about the processes running in parallel is needed, and it is therefore never necessary to generate the com...

This paper presents a novel and generic distributed swarm guidance algorithm using inhomogeneous Markov chains that guarantees superior performance over existing homogeneous Markov chain based algorithms, when the feedback of the current swarm distribution is available. The probabilistic swarm guidance using inhomogeneous Markov chain (PSG–IMC) algorithm guarantees sharper and faster convergenc...

Probabilistic Computation Tree Logic (PCTL) is the established temporal logic for probabilistic verification of discrete-time Markov chains. Probabilistic model checking is a technique that verifies or refutes whether a property specified in this logic holds in a Markov chain. But Markov chains are often infinite or too large for this technique to apply. A standard solution to this problem is t...

This paper presents an anomaly detection technique to detect intrusions into computer and network systems. In this technique, a Markov chain model is used to represent a temporal profile of normal behavior in a computer and network system. The Markov chain model of the norm profile is learned from historic data of the system’s normal behavior. The observed behavior of the system is analyzed to ...

A method for Monte-Carlo simulation of Markov chains is considered. Here, Markov chains are described with a high-level modelling technique called transition classes. State spaces may be very large, Markov chains may be stii, i.e. rare events may occur, simulations can be executed highly in parallel, and hybrid evaluation is possible.