Fast factorisation of probabilistic potentials and its application to approximate inference in Bayesian networks
We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to c...
| Main Authors: | , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/4885 https://doi.org/10.1142/S0218488512500110 |
| Summary: | We present an efficient procedure for factorising probabilistic potentials represented as
probability trees. This new procedure is able to detect some regularities that cannot be
captured by existing methods. In cases where an exact decomposition is not achievable,
we propose a heuristic way to carry out approximate factorisations guided by a parameter
called factorisation degree, which is fast to compute. We show how this parameter can be
used to control the tradeoff between complexity and accuracy in approximate inference
algorithms for Bayesian networks. |
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