The MARKOV.lng Model

Markov Chain Model

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A standard approach used in modeling random variables over time is the Markov chain approach. The basic idea is to think of the system as being in one of a discrete number of states at each point in time. The befavior of the system is described by a transition probability matrix which give the probability the system will move to a specified other state from some given state. Some example situations are:

System | States | Cause of Transition |

Consumer brand switching | Brand of product most recently purchased by consumer | Consumer changes mind, advertising |

Inventory System | Amount of inventory on hand | Orders for new material, demands |

Your company is about to introduce a new detergent and you're interested in whether it will clean up in the market. It will be competing against three other existing brands. In this model, we use Markov chain analysis to determine the long-term, steady state probabilities of the system.

Keywords:

Forecasting | Network | Economic | Equilibrium | Probabilities | Uncertainty | Markov Chain Model | Random Number |