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author | Daniel Wolff |
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date | Fri, 19 Aug 2016 13:07:06 +0200 |
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Daniel@0 | 1 <h2><a name="models">Supported probabilistic models</h2> |
Daniel@0 | 2 <p> |
Daniel@0 | 3 It is trivial to implement all of |
Daniel@0 | 4 the following probabilistic models using the toolbox. |
Daniel@0 | 5 <ul> |
Daniel@0 | 6 <li>Static |
Daniel@0 | 7 <ul> |
Daniel@0 | 8 <li> Linear regression, logistic regression, hierarchical mixtures of experts |
Daniel@0 | 9 |
Daniel@0 | 10 <li> Naive Bayes classifiers, mixtures of Gaussians, |
Daniel@0 | 11 sigmoid belief nets |
Daniel@0 | 12 |
Daniel@0 | 13 <li> Factor analysis, probabilistic |
Daniel@0 | 14 PCA, probabilistic ICA, mixtures of these models |
Daniel@0 | 15 |
Daniel@0 | 16 </ul> |
Daniel@0 | 17 |
Daniel@0 | 18 <li>Dynamic |
Daniel@0 | 19 <ul> |
Daniel@0 | 20 |
Daniel@0 | 21 <li> HMMs, Factorial HMMs, coupled HMMs, input-output HMMs, DBNs |
Daniel@0 | 22 |
Daniel@0 | 23 <li> Kalman filters, ARMAX models, switching Kalman filters, |
Daniel@0 | 24 tree-structured Kalman filters, multiscale AR models |
Daniel@0 | 25 |
Daniel@0 | 26 </ul> |
Daniel@0 | 27 |
Daniel@0 | 28 <li> Many other combinations, for which there are (as yet) no names! |
Daniel@0 | 29 |
Daniel@0 | 30 </ul> |
Daniel@0 | 31 |
Daniel@0 | 32 |