How Hidden Markov Model Works

[rayblood]

hi, can i ask for a little assistance in understanding HMM? i've been reading and watching tutorials about it but none seemed to have discussed it as to my understanding. i am a fourth year university student trying to have a better understanding of HMM. any assistance would be of help. thanks.

 

p.s. also i want to know if bayesian is really needed for HMM to work.

[ecoli]

hi, can i ask for a little assistance in understanding HMM? i've been reading and watching tutorials about it but none seemed to have discussed it as to my understanding. i am a fourth year university student trying to have a better understanding of HMM. any assistance would be of help. thanks.

 

p.s. also i want to know if bayesian is really needed for HMM to work.

 

 

You don't need to construe HMM in a bayesian framework, though it is statistical.

 

The example I like to think of is text prediction, especially for example, when sending a text message from a cell phone (autocomplete). The problem is: can you predict the complete word the author intends from a partial, incomplete word.

 

So you have a partial string, something like: "goodb". the model treats each letter as a node and the transition/edge between the nodes represents the probability that that letter followed from the previous one. This can be calculated from the frequency of letter distribution corpus/dictionary of commonly used words. For example the probability that the character 'u' follows 'q' in standard american english is quite high. two weird consonants 'jz' probably less so.

 

So the user types in "goodb" what is the most probable word? the transition path with the highest probability would [i assume] be "-y-e" followed by an "end" state.

 

Did this help or is this too basic for you?

[rayblood]

the explanation was perfect. thanks for that it made things clearer. i would like to have a follow-up question if you don't mind. the different probabilities associated with the transitions are taken from what kind of source? because i've read a couple of sources and i am quite confused because they have a lot of probabilities incorporated with the different transitions of states. i can't quite find out how these probabilities came about. thanks.

Edited October 1, 2012 by rayblood

[ecoli]

the explanation was perfect. thanks for that it made things clearer. i would like to have a follow-up question if you don't mind. the different probabilities associated with the transitions are taken from what kind of source? because i've read a couple of sources and i am quite confused because they have a lot of probabilities incorporated with the different transitions of states. i can't quite find out how these probabilities came about. thanks.

 

That depends on the application. In the above example, the probability of transition can be calculated by the frequency in which one letter follows another in some word dictionary. For biological sequences, there are similar databases of biological sequences with annotations (where HMM are commonly used to predict function/annotation based on sequence). I'm sure there are many other applications I don't know much about.

[prasl]

Hi, I'm a final year university student and currently doing my final year project. It is basically gesture recognition system. I want to track dynamic hand motions and identify hand gestures. I had try to implement that using HMM. But I couldn't identify starting and end points of the gesture. is any one know how I can track the stating and end points of the gesture. are there any implementations or visual C++ source for achieve this. Please help me to short out this problem and it was a great help for me...

 

Thank you..