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...

This paper deals with the performance analysis of the screening unit in a paper plant using Genetic Algorithm. The screening unit in the paper plant has four main subsystems. These subsystems are arranged in series and parallel configurations. Considering exponential distribution for the probable failures and repairs, the mathematical formulation of the problem is done by Markov birth-death pro...

The present paper deals with development of a simulation model for the performance evaluation of feed water system of a thermal power plant using Markov Birth-Death process and probabilistic approach. In present paper, the feed water system consists of four subsystems. After drawing transition diagram for feed water system, differential equations are developed and then solved recursively using ...

Richardson and Green (1997) present a method of performing a Bayesian analysis of data from a finite mixture distribution with an unknown number of components. Their method is a Markov Chain Monte Carlo (MCMC) approach, which makes use of the “reversible jump” methodology described by Green (1995). We describe an alternative MCMC method which views the parameters of the model as a (marked) poin...

We consider the computing issues of steady probabilities for block-structured discrete-time Markov chains that are upper-Hessenberg or lower-Hessenberg transition kernels with a continuous phase set. An effective computational framework is proposed based on wavelet transform, which extends and modifies arguments in literature quasi-birth-death (QBD) processes. A numerical procedure developed fa...

In this paper, sufficient conditions are given for the existence of limiting distribution of a nonhomogeneous countable Markov chain with time-dependent transition intensity matrix. The method of proof exploits the fact that if the distribution of random process Q = (Qt )t 0 is absolutely continuous with respect to the distribution of ergodic random process Q◦ = (Q◦t )t 0, then Qt law −→ t→∞ wh...

Amatrix analytic paradigm, termed Quasi-Birth-Death Markov chains on binomial-like trees, is introduced and a quadratically converging algorithm to assess its steady state is presented. In a bivariate Markov chain {(Xt, Nt), t ≥ 0}, the values of the variable Xt are nodes of a binomial-like tree of order d, where the i-th child has i children of its own. We demonstrate that it suffices to solve...

The deviation matrix of an ergodic, continuous-time Markov chain with transition probability matrix P (.) and ergodic matrix Π is the matrix D ≡ ∫∞ 0 (P (t)−Π)dt. We give conditions for D to exist and discuss properties and a representation of D. The deviation matrix of a birth-death process is investigated in detail. We also describe a new application of deviation matrices by showing that a me...