# HMMs-1 - Hidden Markov Models(Part 1 BMI/CS 576...

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Hidden Markov Models (Part 1) BMI/CS 576 www.biostat.wisc.edu/bmi576.html Mark Craven [email protected] Fall 2011 A simple HMM A T C G A T C G given say a T in our input sequence, which state emitted it?

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The hidden part of the problem we’ll distinguish between the observed parts of a problem and the hidden parts in the Markov models we’ve considered previously, it is clear which state accounts for each part of the observed sequence in the model above, there are multiple states that could account for each part of the observed sequence – this is the hidden part of the problem Simple HMM for gene finding Figure from A. Krogh, An Introduction to Hidden Markov Models for Biological Sequences GCTAC AAAAA TTTTT CTACG CTACA CTACC CTACT start A T C G A T C G A T C G start
The parameters of an HMM since we’ve decoupled states and characters, we might also have emission probabilities e k ( b ) = P ( x i = b | " i = k ) a kl = P ( i = l | i # 1 = k ) probability of emitting character b in state k probability of a transition from state k to l represents a path (sequence of states) through the model as in Markov chain models, we have transition probabilities ! A simple HMM with emission parameters 0.8 ) A ( 2 e 13 a probability of emitting character A in state 2 probability of a transition from state 1 to state 3 0.4 A 0.4 C 0.1 G 0.2 T 0.3 A 0.1 C 0.4 G 0.4 T 0.1 A 0.2 C 0.3 G 0.3 T 0.2 begin end 0.5 0.5 0.2 0.8 0.6 0.1 0.9 0.2 0 5 4 3 2 1 A 0.4 C 0.1 G 0.1 T 0.4

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Three important questions How likely is a given sequence? the Forward algorithm
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## This note was uploaded on 12/15/2011 for the course BMI 576 taught by Professor Staff during the Fall '11 term at Wisc Green Bay.

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HMMs-1 - Hidden Markov Models(Part 1 BMI/CS 576...

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