[Numeric] ANN: hmm-hmatrix - efficient Hidden Markov Model implementation

[Dominic Steinitz]

Hi Henning,

Thanks for this. I look forward to trying it.

How general are the models that it can fit? For example, a Kalman filter is an example of a hidden Markov model in which the updates are linear and the errors are Gaussian. I wrote a few notes on such models although I have yet to follow this up: https://idontgetoutmuch.wordpress.com/2015/06/20/some-background-on-hidden-markov-models/ <https://idontgetoutmuch.wordpress.com/2015/06/20/some-background-on-hidden-markov-models/>. As you can see, I don’t think the wikipedia definition is correct but maybe this is a matter of taste / definition although I often find wikipedia slightly misses the point on some mathematical topics.

I also uploaded an extended Kalman filter package here: https://hackage.haskell.org/package/Kalman <https://hackage.haskell.org/package/Kalman>.

You might also be interested in a version of haddock I have which renders mathematics correctly. For example, see here: https://hackage.haskell.org/package/Kalman-0.1.0.1/docs/Kalman.html <https://hackage.haskell.org/package/Kalman-0.1.0.1/docs/Kalman.html>.

Dominic Steinitz
dominic at steinitz.org
http://idontgetoutmuch.wordpress.com

> On 19 Aug 2015, at 12:06, Henning Thielemann <lemming at henning-thielemann.de> wrote:
> 
> 
> My HMM implementation is on Hackage and has already proven to be useful:
>   https://hackage.haskell.org/package/hmm-hmatrix
> 
> It supports Discrete and Gaussian models and can be extended to other moduls using the type classes from Distribution module. The package implements supervised and unsupervised training, as well as a training using a predefined distribution and patterns. It also supports mixing of trainings. Trained models can be read from and written to CSV. The features are demonstrated by three simple examples.
> 
> 
> https://en.wikipedia.org/wiki/Hidden_Markov_Model
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