Suppose the weather on any day depends on the weather conditions of the previous two days. More precisely, suppose the weather can only be sunny (S) or cloudy (C). If it was sunny today and yesterday, it will be sunny tomorrow with probability 0.8. If it is sunny today and cloudy yesterday, it will be sunny tomorrow with probability 0.6. If it is cloudy today but sunny yesterday, it will be sunny tomorrow with probability 0.4, and if it cloudy for the last two days, it will be sunny tomorrow with probability 0.1. Such a model can be turned to a Markov chain whose current state is the weather both today and yesterday. Consider = {(S, C), (S, S), (C, S), (C, C)}, where (S, C) means sunny yesterday but cloudy today