HMM-intro - Hidden Markov Model Most pages of the slides...

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Hidden Markov Model • Most pages of the slides are from lecture notes from Prof. Serafim Batzoglou s course in Stanford: – CS 262: Computational Genomics (Winter 2004) http://ai.stanford.edu/~serafim/cs262/
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Hidden Markov Models 1 2 K 1 2 K 1 2 K 1 2 K x 1 x 2 x 3 x K
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Applications of hidden Markov models HMMs can be applied in many fields where the goal is to recover a data sequence that is not immediately observable (but other data that depends on the sequence is). – Cryptanalysis Speech recognition Machine translation Partial discharge Gene prediction Alignment of bio-sequences * reference: wikipiedia.com
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Example: The Dishonest Casino A casino has two dice: Fair die P(1) = P(2) = P(3) = P(4) = P(5) = P(6) = 1/6 Loaded die P(1) = P(2) = P(3) = P(4) = P(5) = 1/10 P(6) = 1/2 Casino player switches back-&-forth between fair and loaded die once roughly around every 20 turns Game: 1. You bet $1 2. You roll (always with a fair die) 3. Casino player rolls (maybe with fair die, maybe with loaded die) 4. Highest number wins $2
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The dishonest casino model FAIR LOADED 0.05 0.05 0.95 0.95 P(1|F) = 1/6 P(2|F) = 1/6 P(3|F) = 1/6 P(4|F) = 1/6 P(5|F) = 1/6 P(6|F) = 1/6 P(1|L) = 1/10 P(2|L) = 1/10 P(3|L) = 1/10 P(4|L) = 1/10 P(5|L) = 1/10 P(6|L) = 1/2
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Question # 1 – Evaluation GIVEN
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This note was uploaded on 01/14/2012 for the course CDA 6530 taught by Professor Zou during the Fall '11 term at University of Central Florida.

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HMM-intro - Hidden Markov Model Most pages of the slides...

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