Learning recursive probability trees from probabilistic potentials
A recursive probability tree (RPT) is an incipient data structure for representing the distributions in a probabilistic graphical model. RPTs capture most of the types of independencies found in a probability distribution. The explicit representation of these features using RPTs simplifies computati...
| Main Authors: | , , , , |
|---|---|
| Format: | info:eu-repo/semantics/report |
| Language: | English |
| Published: |
2012
|
| Online Access: | http://hdl.handle.net/10835/1549 |
| Summary: | A recursive probability tree (RPT) is an incipient data structure for representing the distributions in a probabilistic graphical model. RPTs capture most of the types of independencies found in a probability distribution. The explicit representation of these features using RPTs simplifies computations during inference. This paper describes a learning algorithm that builds a RPT from a probability distribution. Experiments prove that this algorithm generates a good approximation of the original distribution, thus making available all the advantages provided by RPTs |
|---|