lect 05 HMM

lect 05 HMM - Hidden Markov models (HMM) Instructor Click...

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Click to edit Master subtitle style 7/29/10 Hidden Markov models (HMM) Instructor Dr. Junwen Wang
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Click to edit Master subtitle style 7/29/10 Bioc 2808 Lecture 5 Hidden Markov models Instructor Dr. Junwen Wang
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7/29/10 Review of last lecture Content sensor and signal sensor Generate a sequence based on 0th or 1st order Markov chains (MC) Score a sequence based on MC Classify sequence based on the scores from different models Train and test higher order Markov chains
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7/29/10 Today’s topics Introduction to Hidden Markov model (HMM), their applications Three major functions of HMM Given a model, how to calculate probability of a sequence Given a sequence, how to decode the its hidden state How to train the HMM
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7/29/10 After the class, you are expected to Know the concept of HMM and its applications Score a sequence by HMM Find the hidden states of a sequence How to build a HMM
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7/29/10 66 HMM …… HMM M C …… Observed layer Hidden Layer
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7/29/10 Applications of HMM (I) Protein secondary structure prediction Gene prediction
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7/29/10 Applications of HMM (II) Protein sequence alignment Protein domain recognition
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7/29/10 Applications of HMM (III) Decoding the music Gene prediction Decoding the speech
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7/29/10 A toy HMM (I) Mood (hidden) Expressi on Smile smile cry cry Happy happy unhappy unhappy
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A toy HMM (II) 0.8 0.2 0.1 0.9 3. Emission probabilities (E) Happy (H) Unhappy (U) 1. Finite set of hidden states Q={H,U} Smile (S) Cry (C) 2. Finite set emission alphabet α={S,C} 0 . 8 0 . 2 0 . 5 0 4. Transition probabilities (A) 5. Initial probabilities sta rt 0. 7
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lect 05 HMM - Hidden Markov models (HMM) Instructor Click...

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