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Markov Analysis

Markov analysis refers to a method used to predict the future of a random variable basing it on the current position or behavior. Named after the Russian mathematician, Andrey Markov, the technique solely focusses on the current circumstances of the random variable and does not rely on other factors that led to the current position or behavior, an assumption that the transition rates are constant. The method allows the calculations of the future probability of the actions from the current state, by fulfilling the Markov’s property (memoryless). The random variable satisfies the Markov property if it is independent of the history; the future can be predicted by observing the present characteristics.

Process taken in Markov Analysis

The transition from one stage to another in Markov processes is defined by a transition matrix. A sequence of transition matrices leads to a Markov chain which is a stochastic model. Markov chains describe Markov processes that contain discrete index set or discrete state space. the Markov’s chain changes randomly which makes it impossible to predict the state of the chain at a given time in the future. However, the statistical properties of the given chain can be determined in an important statement. Markov chain defines a series of events in machinery that will be able to determine the repair and maintenance programs.

Application of Markov Analysis

The applications of these techniques are used in a wide range of applications from the manufacturing processes in the industries to making decisions in the business circles.  In the industry, if it is possible to determine the production of faulty products today, we can use the number to determine the number of faulty products in future upsilon the Markov analysis with the current status. Markov analysis is also widely used in the marketing of goods. The marketing executives conduct researches on consumers are loyal to a particular brand and use that loyalty to model their behavior to other products that the company will produce in the future. The Markov analysis assumes that consumers do not just switch from a brand to another brand at random but base reasons on the past choices. Therefore, knowing the past choices that a company can predict how the goods will be received by the consumers in the market place.

Other applications of Markov’s analysis included in the health sector in the production of a number of patients, the behavior of stock prices, human needs among others. The limits of Markov chains and analysis is that Markov diagrams for large systems are difficult to construct and analyze.