Dynamic Hybrid Random Fields for the Probabilistic Graphical Modeling of Sequential Data: Definitions,Algorithms, and an Application to Bioinformatics |
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Authors: | Marco Bongini Antonino Freno Vincenzo Laveglia Edmondo Trentin |
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Affiliation: | 1.DIISM,Università di Siena,Siena,Italy;2.Zalando SE,Berlin,Germany;3.DINFO,Università di Firenze,Florence,Italy |
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Abstract: | The paper introduces a dynamic extension of the hybrid random field (HRF), called dynamic HRF (D-HRF). The D-HRF is aimed at the probabilistic graphical modeling of arbitrary-length sequences of sets of (time-dependent) discrete random variables under Markov assumptions. Suitable maximum likelihood algorithms for learning the parameters and the structure of the D-HRF are presented. The D-HRF inherits the computational efficiency and the modeling capabilities of HRFs, subsuming both dynamic Bayesian networks and Markov random fields. The behavior of the D-HRF is first evaluated empirically on synthetic data drawn from probabilistic distributions having known form. Then, D-HRFs (combined with a recurrent autoencoder) are successfully applied to the prediction of the disulfide-bonding state of cysteines from the primary structure of proteins in the Protein Data Bank. |
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