Markov Analysis

Markov Analysis is a method used to forecast the value of a variable whose future value is influenced only by its current position or state, not by any prior activity that led the variable to its current position or state. In essence, it forecasts the activity of a random variable based solely upon the current circumstances surrounding the random variable.

The technique is named after Russian mathematician Andrei Andreyevich Markov, who pioneered the study of stochastic processes, which are processes that involve the operation of chance. He first used the process to predict the behavior of gas particles trapped in an enclosed container. The Markov Analysis process is a method for forecasting random variables, and is often used for predicting behaviors and decisions within large groups of people.

The Markov Analysis process involves defining the likelihood of a future action given the current state of a variable. Once the probabilities of future actions at each state are defined, a decision tree can be drawn and the probability of a result can be calculated given the current state of a variable. Markov Analysis has a number of applications in the business world. It is often used to predict the number of defective pieces that will come off of an assembly line given the operating status of the machines on the line.

It can also be used to predict the proportion of a company's accounts receivables that will become bad debts. Certain stock price and option price forecasting methods also incorporate Markov Analysis. Lastly, companies often use it to forecast future brand loyalty of current customers and the outcome of these consumer decisions on a company's market share.