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- Markov chain
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Explore more content. Modelling manufacturing processes using Markov chains. Cite Download Optimizing manufacturing processes with inaccurate models of the process will lead to unre-liable results. This can be true when there is a strong human influence on the manufacturing process and many variable aspects. This study investigates modelling a manufacturing process influenced by human inter-action with very variable products being processed. To develop a more accurate process model for such pro-cesses radio frequency identification RFID tags can be used to track products through the process.

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This paper proposes an extension of a single coupled Markov chain model to characterize heterogeneity of geological formations, and to make conditioning on any number of well data possible. The methodology is based on the concept of conditioning a Markov chain on the future states. Because the conditioning is performed in an explicit way, the methodology is efficient in terms of computer time and storage. Applications to synthetic and field data show good results. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve.

A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, [1] [4] [5] [6] such as studying cruise control systems in motor vehicles , queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics. Markov processes are the basis for general stochastic simulation methods known as Markov chain Monte Carlo , which are used for simulating sampling from complex probability distributions, and have found application in Bayesian statistics , thermodynamics , statistical mechanics , physics , chemistry , economics , finance , signal processing , information theory and artificial intelligence. The adjective Markovian is used to describe something that is related to a Markov process. A Markov process is a stochastic process that satisfies the Markov property [1] sometimes characterized as " memorylessness ". In simpler terms, it is a process for which predictions can be made regarding future outcomes based solely on its present state and—most importantly—such predictions are just as good as the ones that could be made knowing the process's full history.

Request PDF | On Jul 22, , Bruno Sericola published Markov Chains. Theory, Algorithms and Applications | Find, read and cite all the research you need on.

OpenStax CNX. Jun 9, Creative Commons Attribution License 1. This material has been modified by Roberta Bloom, as permitted under that license. A Markov chain can be used to model the status of equipment, such as a machine used in a manufacturing process.

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A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. It is named after the Russian mathematician Andrey Markov. Markov chains have many applications as statistical models of real-world processes, [1] [4] [5] [6] such as studying cruise control systems in motor vehicles , queues or lines of customers arriving at an airport, currency exchange rates and animal population dynamics.

The joint asymptotic distribution is derived for certain functions of the sample realizations of a Markov chain with denumerably many states, from which the joint asymptotic distribution theory of estimates of the transition probabilities is obtained. Application is made to a goodness of fit test. Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide.

It seems that you're in Germany. We have a dedicated site for Germany. Kronecker products are used to define the underlying Markov chain MC in various modeling formalisms, including compositional Markovian models, hierarchical Markovian models, and stochastic process algebras. The motivation behind using a Kronecker structured representation rather than a flat one is to alleviate the storage requirements associated with the MC. With this approach, systems that are an order of magnitude larger can be analyzed on the same platform.

The joint asymptotic distribution is derived for certain functions of the sample realizations of a Markov chain with denumerably many states, from which the joint asymptotic distribution theory of estimates of the transition probabilities is obtained. Application is made to a goodness of fit test. Most users should sign in with their email address. If you originally registered with a username please use that to sign in.

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## 4 Comments

## Crisrom

theory underlying Markov chains and the applications that they have. To this end, we will review some basic, relevant probability theory. Then we will progress to.

## Gaspar J.

Markov chains are a fundamental class of stochastic processes. They are widely used to solve problems in a large number of domains such as operational.

## Tisha M.

Andersson, Introduktion ). Limitations and Purposes. This paper will not explore very deep theory regarding Markov's Chain; instead, the.

## Evie W.

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