Markov Chain Analysis

We learnt **Markov Chain Analysis**, with an example on Market Share Analysis. To reiterate, Markov Chain Analysis is a technique to find probabilities of future state of an event, by knowing current state and probability of transition between states. It is applicable in cases, where an event can take any of the multiple possible states. As a simple example, weather can be sunny, rainy or cloudy and we know the transition probability between each pair. If it is sunny today, it can be sunny, rainy or cloudy on subsequent day with probabilities of 0.7, 0.1 and 0.2 respectively. We can find the probability of weather being sunny, rainy or cloudy at any future day using Markov Chain Analysis.

This technique has several applications, like Market Share analysis, Machine failure, Weather forecast, Bond Rating movement and so on.

Now we would apply our learning, with an example of Bond Rating movement with the help of the Self-service Tool.