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  • https://bio.libretexts.org/Bookshelves/Computational_Biology/Book%3A_Computational_Biology_-_Genomes_Networks_and_Evolution_(Kellis_et_al.)/07%3A_Hidden_Markov_Models_I/7.03%3A_Markov_Chains_and_HMMS-_From_Example_to_Formalizing
    This is a “what you see is what you get” in that the next state only depends on the current state and there is no memory of the previous state. P(xL) can also be calculated from the transition pro...This is a “what you see is what you get” in that the next state only depends on the current state and there is no memory of the previous state. P(xL) can also be calculated from the transition probabilities: If we multiply the initial state probabilities at time t = 0 by the transition matrix A, we get the probabilities of states at time t = 1.

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