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What is Markov Process

Encyclopedia of Business Analytics and Optimization
A stochastic process where the probabilities of the events depend on the previous event only.
Published in Chapter:
Business Processes, Dynamic Contexts, Learning
Michael M. Richter (University of Calgary, Canada)
Copyright: © 2014 |Pages: 11
DOI: 10.4018/978-1-4666-5202-6.ch037
Abstract
In this article we present relations between complex business processes and machine learning techniques. The processes considered here are mostly related to planning. Planning takes place in preparing many decisions and often it is encountered with a rapidly changing context that constitutes an open world. The underlying structure and preconditions of the processes is quite often not known and hence the processes are regarded as stochastic. One can only observe the processes. Such observations deliver data and these data contain some knowledge about the processes in a hidden form. As a consequence, machine learning methods are involved here. The idea is to give the business persons an overview of quite different machine learning techniques so that they can select suitable ones. We provide a number of examples for business processes that we use for illustrations.
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More Results
Historic Perspective of Log Analysis
A stochastic process in which the transition probabilities can be estimated on the basis of first order data. Such a process is also stationary in that probability estimates do not change across the sample (generally across time)
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A Longitudinal Analysis of Labour Market Data with SOM
Stochastic process in wich the new state of a system depends on the previous state or a finite set of previous states.
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