Generating data using Hidden Markov Models
A Hidden Markov Model (HMM) is a powerful analysis technique for analyzing sequential data. It assumes that the system being modeled is a Markov process with hidden states. This means that the underlying system can be one among a set of possible states. It goes through a sequence of state transitions, thereby producing a sequence of outputs. We can only observe the outputs but not the states. Hence these states are hidden from us. Our goal is to model the data so that we can infer the state transitions of unknown data.
In order to understand HMMs, let's consider the example of a salesman who has to travel between the following three cities for his job -- London, Barcelona, and New York. His goal is to minimize the traveling time so that he can be more efficient. Considering his work commitments and schedule, we have a set of probabilities that dictate the chances of going from city X to city Y. In the information given below, P(X -> Y...
