Markov chains in random environment with applications in queuing theory and machine learning

qfd planning with cost consideration in fuzzy environment

در عصر حاضر که رقابت بین سازمان ها بسیار گسترش یافته است، مطالعه و طرحریزی سیستم های تولیدی و خدماتی به منظور بهینه سازی عملکرد آنها اجتناب ناپذیر می باشد. بخش عمده ای از رقابت پذیری سازمان ها نتیجه رضایتمندی مشتریان آنها است. میزان موفقیت سازمان های امروزی به تلاش آنها در جهت شناسایی خواسته ها و نیازهای مشتریان و ارضای این نیازها بستگی دارد. از طرفی کوتاه کردن زمان ارائه محصول/خدمات به مشتریان...

Monotonicity in Markov Reward and Decision Chains: Theory and Applications

This paper focuses on monotonicity results for dynamic systems that take values in the natural numbers or in more-dimensional lattices. The results are mostly formulated in terms of controlled queueing systems, but there are also applications to maintenance systems, revenue management, and so forth. We concentrate on results that are obtained by inductively proving properties of the dynamic pro...

Gaussian Markov Random Fields: Theory and Applications

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...

Stacked Graphical Learning: Learning in Markov Random Fields using Very Short Inhomogeneous Markov Chains

We described stacked graphical learning, a meta-learning scheme in which a base learner is augmented by expanding one instance’s features with predictions on other related instances. The stacked graphical learning is efficient, especially during inference, capable of capturing dependencies easily, and can be constructed based on any kind of base learner. In experiments on two classification pro...