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I've been looking for the internet explanation about the hidden Markov model (HMM) and its implementation but I think there's not quite good explanation. Does anyone know and guide me through this like tutorial and its implementation. There will be feedback and not do this as charity.

So I hope there will be someone interested with this subject please contact me very further so we can talk soon. Thank you very much.

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closed as off topic by user88 Apr 1 '12 at 12:15

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  • $\begingroup$ This is not a place for hiring tutors. Please read the FAQ. $\endgroup$ – user88 Apr 1 '12 at 12:15
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I once had to do a presentation for Hidden Markov Models at my university from scratch and had to self learn it. A good source for start understanding is the well known Artificial Intelligence: A Modern Approach (3rd Edition) book. Note that I am not suggesting you to read the whole book at all, there is a chapter that talks about HMM and I was fine on understanding the examples and the idea despite not having background on either AI or statistics.

Looking into research papers could be useful to grasp few examples, such as this one. I find the AI book example particular useful about having the idea of inferring the weather by only know someone is carrying or not a umbrella in a given day.

For implementation, see this. I used this code myself and actually tried to understand it on my own despite not knowing C++. The website provides examples you can see the theory being applied as well, which was very interesting for me. You might want to be skeptical on the AI book regarding the probability proofs before getting a full picture of HMM. I also noticed that one approach about HMM reasons more about matrixes and other is more visual such as the paper linked here that consider state machines. I find states machines easier to understand for beginners.

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  • $\begingroup$ do you have IM ? $\endgroup$ – Gordon Blue Apr 1 '12 at 7:07
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Given that HMMs can be viewed as dynamic Bayesian networks, I'd recommend getting your feet wet with this free course on probabilistic graphical models (offered through Stanford). As part of the first week's content there are lectures and problems on template models which may help serve as a gentle introduction to dynamic Bayes nets.

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