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Results tagged with hidden-markov-model
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user 17096
Hidden Markov Models are used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states.
2
votes
Accepted
How to handle new observations on HMM decoding?
The problem you're referring to, often called data sparsity, arises often in language modeling. In particular, if some vocabulary words don't appear in your training corpus then maximum likelihood te …
- 1,259
16
votes
Definition of dynamic Bayesian system, and its relation to HMM?
I'd recommend looking through these two excellent review papers:
An Introduction to Hidden Markov Models and Bayesian Networks by
Zoubin Gharamani
Dynamic Bayesian Networks by Kevin
Murphy
HMMs …
- 1,259
1
vote
HMM a posteriori probs for hidden states and more
To get a distribution over the hidden state sequence use the forward-backward algorithm rather than viterbi.
Regarding the problem of inferring too many 1s in your hidden sequence, you should think …
- 1,259
4
votes
Accepted
Mathematics needed to understand Hidden Markov Models?
Basic probability theory (i.e. when to sum versus multiply probabilities) is the only essential requirement. For Bayesian HMMs you would of course need to understand Bayesian inference, but HMMs do n …
- 1,259
1
vote
Accepted
Training Hidden Markov Models for multiple input observations
In that case, you would typically add a special "start" and "stop" symbol to the beginning and end of each sequence, and then concatenate them. Then define a special start and stop state which only ou …
- 1,259
2
votes
Accepted
Initial Probabilities of an HMM
If you're training the HMM on one long string then it only has one example of a transition from the start state, thus your initial transition probability is rather meaningless. To get a meaningful est …
- 1,259