DagSemProc.05051.6.pdf
- Filesize: 306 kB
- 8 pages
Document
Exploiting independence for branch operations in Bayesian learning of C&RTs
Authors
-
Part of:
Volume:
Dagstuhl Seminar Proceedings, Volume 5051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-02-08
Cite AsGet BibTex
Nicos Angelopoulos and James Cussens. Exploiting independence for branch operations in Bayesian learning of C&RTs. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)
https://doi.org/10.4230/DagSemProc.05051.6
https://doi.org/10.4230/DagSemProc.05051.6
Abstract
In this paper we extend a methodology for Bayesian learning via MCMC,
with the ability to grow arbitrarily long branches in C&RT
models. We are able to do so by exploiting independence in the
model construction process. The ability to grow branches rather
than single nodes has been noted as desirable in the literature.
The most singular feature of the underline methodology used here
in comparison to other approaches is the coupling of the prior
and the proposal. The main contribution of this paper is to show
how taking advantage of independence in the coupled process, can allow
branch growing and swapping for proposal models.
Keywords
- Bayesian machine learning
- classification and regression trees
- stochastic logic programs
Metrics
- Access Statistics
-
Total Accesses (updated on a weekly basis)
0PDF Downloads