For example the following left chain will have

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Unformatted text preview: ve binomial distribution”: 0.08 0.06 0.04 0.02 0 Prof. Jeff Bilmes 10 20 30 40 50 60 d EE596A/Winter 2013/DGMs – Lecture 4 - Jan 23rd, 2013 page 4-72 (of 239) HMMs HMMs as GMs Other HMM queries What HMMs can do MPE Summ State Duration Modeling If we have multiple parallel states in series, all of which share the same observation distribution, we can construct much more interesting (multimodal) distributions. Prof. Jeff Bilmes EE596A/Winter 2013/DGMs – Lecture 4 - Jan 23rd, 2013 page 4-73 (of 239) HMMs HMMs as GMs Other HMM queries What HMMs can do MPE Summ State Duration Modeling If we have multiple parallel states in series, all of which share the same observation distribution, we can construct much more interesting (multimodal) distributions. For example, the following left chain will have distribution as shown on the right (a mixture of negative binomial distributions). Prof. Jeff Bilmes EE596A/Winter 2013/DGMs – Lecture 4 - Jan 23rd, 2013 page 4-73 (of 239) HMMs HMMs as GMs Other HMM queries What HMMs can do MPE Summ State Duration Modeling If we have multiple parallel states in series, all of which share the same observation distribution, we can construct much more interesting (multimodal) distributions. For example, the following left chain will have distribution as shown on the right (a mixture of negative binomial distributions). Prof. Jeff Bilmes EE596A/Winter 2013/DGMs – Lecture 4 - Jan 23rd, 2013 page 4-73 (of 239) HMMs HMMs as GMs Other HMM queries What HMMs can do MPE Summ State Duration Modeling If we have multiple parallel states in series, all of which share the same observation distribution, we can construct much more interesting (multimodal) distributions. For example, the following left chain will have distribution as shown on the right (a mixture of negative binomial distributions). 0.6 0.6 0.4 0.6 0.6 0.4 0.4 0.4 0.2 0.99 0.8 Prof. Jeff Bilmes 0.99 0.01 0.99 0.01 0.99 0.01 0.01 EE596A/Winter 2013/DGMs – Lecture 4 - Jan 23rd, 2013 page 4-73 (of 239) HMMs HMMs as GMs Other HMM queries What HMMs can do MPE Summ State Duration Modeling If we have multiple parallel states in series, all of which share the same observation distribution, we can construct much more interesting (multimodal) distributions. For example, the following left chain will have distribution as shown on the right (a mixture of negative binomial distributions). 0.025 0.6 0.6 0.4 0.6 0.6 0.4 0.02 0.4 0.4 0.2 0.99 0.8 0.99 0.01 0.99 0.01 0.015 0.99 0.01 0.01 0.01 0.005 Prof. Jeff Bilmes 10 20 30 d EE596A/Winter 2013/DGMs – Lecture 4 - Jan 23rd, 2013 40 50 60 page 4-73 (of 239) HMMs HMMs as GMs Other HMM queries What HMMs can do MPE Summ State Duration Modeling If we have multiple parallel states in series, all of which share the same observation distribution, we can construct much more interesting (multimodal) distributions. For example, the following left chain will have distribution as shown on the right (a mixture of negative binomial distributions). 0.025...
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