The problem we investigate deals with the optimal assignment of resources to the activities of a stochastic project network. We seek to minimize the expected cost of the project include sum of resource utilization costs and lateness costs. We assume that the work content required by the activities follows an exponential distribution. The decision variables of the model are the allocated resourc...

Introduction: The rapid urbanization coupled with industrial development in Indian cities has led to air pollution that causes adverse effects on the health of human beings. So, it is crucial to track the quality of air in industrial areas of a city to insulate the public from harmful air pollutants.  The present study examined and predicted air quality index levels in industrial areas located ...

Introduction MCMCMS implements a generic framework for constructing Markov chains. It can be used to perform statistical machine learning in a Bayesian framework. It presents a modular, high level approach to performing MCMC simulations over statistical models that can explain observed data [1]. The two main benefits of MCMCMS are its prior-centric construction of the chain, that does away with...

Recently, the Markov chain choice model has been introduced by Blanchet et al. to overcome the computational intractability for learning and revenue management for several modern choice models, including the mixed multinomial logit models. However, the known methods for learning the Markov models require almost all items to be offered in the learning stage, which is impractical. To address this...

Phytoplankton, as the first level of food accessible to animals, is the main source of nutrient in the ocean. That is why the understanding of its behaviour becomes so important. Properties of phytoplankton have been widely investigated by researchers from various branches of science. Phytoplankton cells have the ability to form aggregates— groups of cells bonded together. Plankton cells in suc...

The work described in this paper is an investigation of applying Markov chain techniques to measure software reliability. An example is taken from database based application software to develop two stochastic models called usage model and testing model for the software. The log likelihood ratio i.e. D (U, T) of two stochastic processes tells us how similar these processes are. This information ...

Probabilistic compositional models, similarly to graphical Markov models, are able to represent multidimensional probability distributions using factorization and closely related concept of conditional independence. Compositional models represent an algebraic alternative to the graphical models. The system of related conditional independencies is not encoded explicitly (e.g. using a graph) but ...

We introduce a bivariate Markov chain counting process with contagion for modelling the clustering arrival of loss claims with delayed settlement for an insurance company. It is a general continuous-time model framework that also has the potential to be applicable to modelling the clustering arrival of events, such as jumps, bankruptcies, crises and catastrophes in finance, insurance and econom...

The work described in this paper is an investigation of applying Markov chain techniques to measure software reliability. An example is taken from database based application software to develop two stochastic models called usage model and testing model for the software. The log likelihood ratio i.e. D (U, T) of two stochastic processes tells us how similar these processes are. This information ...