Learning Conditional Distributions using Mixtures of Truncated Basis Functions
Mixtures of Truncated Basis Functions (MoTBFs) have recently been proposed for modelling univariate and joint distributions in hybrid Bayesian networks. In this paper we analyse the problem of learning conditional MoTBF distributions from data. Our approach utilizes a new technique for learning...
| Autori principali: | , , |
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| Natura: | info:eu-repo/semantics/article |
| Lingua: | English |
| Pubblicazione: |
2017
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| Accesso online: | http://hdl.handle.net/10835/4859 |
| Riassunto: | Mixtures of Truncated Basis Functions (MoTBFs) have recently
been proposed for modelling univariate and joint distributions
in hybrid Bayesian networks. In this paper we analyse the problem of
learning conditional MoTBF distributions from data. Our approach utilizes
a new technique for learning joint MoTBF densities, then propose
a method for using these to generate the conditional distributions. The
main contribution of this work is conveyed through an empirical investigation
into the properties of the new learning procedure, where we also
compare the merits of our approach to those obtained by other proposals. |
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