Markov Chains, Probabilistic Models, Text Generation, Hidden Markov Models, Python.
chains can be described as memoryless. Each transition is associated with a certain probability, as in the above image. For example, if the current state is A and the probability of E being the next state is 0.4. Also, the sum of all the transition probabilities of a state should be equal to 1.