On Parameter Priors for Discrete DAG Models
Abstract
We investigate parameter priors for discrete DAG models. It was shown in previous works that a Dirichlet prior on the parameters of a discrete DAG model is inevitable assuming global and local parameter independence for all possible complete DAG structures. A similar result for Gaussian DAG models hinted that the assumption of local independence may be redundant. Herein, we prove that the local independence assumption is necessary in order to dictate a Dirichlet prior on the parameters of a discrete DAG model. We explicate the minimal set of assumptions needed to dictate a Dirichlet prior, and we derive the functional form of prior distributions that arise under the global independence assumption alone.
Cite
Text
Rusakov and Geiger. "On Parameter Priors for Discrete DAG Models." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.Markdown
[Rusakov and Geiger. "On Parameter Priors for Discrete DAG Models." Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.](https://mlanthology.org/aistats/2001/rusakov2001aistats-parameter/)BibTeX
@inproceedings{rusakov2001aistats-parameter,
title = {{On Parameter Priors for Discrete DAG Models}},
author = {Rusakov, Dmitry and Geiger, Dan},
booktitle = {Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics},
year = {2001},
pages = {259-264},
volume = {R3},
url = {https://mlanthology.org/aistats/2001/rusakov2001aistats-parameter/}
}