Suppose now we work/live in a windowless building. Each transition probability is just the number of transitions of that type we observed divided by 100. How do we estimate values of the transition probabilities in this model? We go outside and observe the weather for, say, 100 days. So, for example, we need the probably that tomorrow will be sunny given that today is sunny, the probably that tomorrow will be cloudy given that today is sunny, and so on. There will be nine of these (because there are three states). Then, we pick a transition period (let's assume 1 day) and determine estimates of the transition probabilities. We classify days as, say, sunny, cloudy, or raining. The classic textbook example of a Markov model is the weather. Lets start with an ordinary Markov model and then tweak it to create a hidden Markov model.
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