Factorisation of Probability Trees and its Applications
Bayesian networks can be seen as a factorisation of a joint probability distribution over a set of variables, based on the conditional independence relations amongst the variables. In this paper we show how it is possible to achieve a finer factorisation decomposing the origninal factors in which...
| Main Authors: | , , , |
|---|---|
| Format: | info:eu-repo/semantics/report |
| Language: | English |
| Published: |
2012
|
| Online Access: | http://hdl.handle.net/10835/1554 |
| Summary: | Bayesian networks can be seen as a factorisation of a joint probability distribution over a set of variables, based on the conditional independence relations amongst the variables. In this paper we show how it is possible to achieve a finer factorisation decomposing the origninal factors in which some conditions hols. The new ideas can be applied to algorithms able to deal wih factorised probabilistic potentials, as Lazy Propagation, Lazy-Penniless and Importance Sampling. |
|---|