Markov chains with replacement

A new machine replacement policy based on number of defective items and Markov chains

  A novel optimal single machine replacement policy using a single as well as a two-stage decision making process is proposed based on the quality of items produced. In a stage of this policy, if the number of defective items in a sample of produced items is more than an upper threshold, the machine is replaced. However, the machine is not replaced if the number of defective items is less than ...

Empirical Bayes Estimation in Nonstationary Markov chains

Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical  Bayes estimators  for the transition probability  matrix of a finite nonstationary  Markov chain. The data are assumed to be of  a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...

On sampling with Markov chains

Exact Sampling with Markov Chains

Random sampling has found numerous applications in computer science, statistics, and physics. The most widely applicable method of random sampling is to use a Markov chain whose steady state distribution is the probability distribution r from which we wish to sample. After the Markov chain has been run for long enough, its state is approximately distributed according to 7r. The principal proble...

Importance Analysis with Markov Chains

In order to maximize system dependability improvements we need criteria for placement of component redundancy. One such criterion is based on quantitative measures provided by importance theory. Importance coefficients of components in mathematical models provide numerical ranks based on the contribution of the component to a system event occurrence (i.e., the one for which the model was constr...