due to the effective role of markov models in customer relationship management (crm), there is a lack of comprehensive literature review which contains all related literatures. in this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. one hundred articles were identified and reviewed to find direct relevance for applying markov models...

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...

In this paper, the ambiguity of nite state irreducible Markov chain trajectories is reminded and is obtained for two state Markov chain. I give an applicable example of this concept in President election

In this paper, we develop and apply Bayesian inference for an extended NelsonSiegel (1987) term structure model capturing interest rate risk. The so-called Stochastic Volatility Nelson-Siegel (SVNS) model allows for stochastic volatility in the underlying yield factors. We propose a Markov chain Monte Carlo (MCMC) algorithm to efficiently estimate the SVNS model using simulation-based inference...

We build an empirical model under the stochastic frontier framework to assess production efficiencies of container operators from the world's major container ports in the years between 1997 and 2004. The empirical model measures efficiencies, efficiency changes, and time-persistence of efficiencies after controlling for the individual heterogeneity in technology and technical change. The model ...

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...

Results are summarized from the literature on three commonly used stochastic population models with regard to persistence time. In addition, several new results are introduced to clearly illustrate similarities between the models. Specifically, the relations between the mean persistence time and higher-order moments for discrete-time Markov chain models, continuous-time Markov chain models, and...

in a finite stationary markov chain, transition probabilities may depend on some explanatoryvariables. a similar problem has been considered here. the corresponding posteriors are derived andinferences are done using these posteriors. finally, the procedure is illustrated with a real example.

A semiflexible Gaussian chain model is used to determine the statistics and correlations of single-molecule fluorescence resonant energy transfer ~FRET! experiments on biological polymers. The model incorporates a persistence length in a Rouse chain and describes single-chain dynamics with normal modes. The hydrodynamic interaction is included in the dynamics of the semiflexible Gaussian chain ...