Detailed modeling of a grinding mill can be achieved through Markov chain models without involving lengthy computations. However, estimation of the key parameters of the model, elements of the Markov transition matrix, using observable quantities is not trivial. This powerful modeling tool can find wide applicability in operation and control of industrial mills if this set of parameters can be ...

By using the optimum-interval interpolation estimation, in this paper, a new composite model which is based on the improved second generation wavelet transform and second order nonhomogeneous Hidden Markov Model(ISGWT-SNHMM) is proposed and applied in the power quality disturbance classification. By adopting the interpolating scheme and the optimum-interval interpolation estimation, the predict...

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.

We present a composite Compressed Sensing system for the acquisition and recovery of compressible signals, where sparse Binary Matrix aids Sparsity Order Estimation, Gaussian reconstruction. The is deterministic adapted according to varying nature sparsity order. estimate order by exploiting structure statistics obtained measurements. refine estimates using Kalman filter with discrete Markov mo...

We  deal with the relationship termination problem in the context of individual-level customer relationship management (CRM) and use a Markov decision process to determine the most appropriate occasion for termination of the relationship with a seemingly unprofitable customer. As a particular case, the beta-geometric/beta-binomial model is considered as the basis to define customer beha...

Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood est...

‎the aim of this paper is to study distribution of ratios of generalized order statistics from pareto distribution. parameter estimation of pareto distribution based on generalized order statistics and ratios of them have been obtained. inferences using method of moments and unbiased estimator have been obtained to develop point estimations. consistency of unbiased estimator has been illustrate...