Probability estimation is one of the fundamental tasks in statistics and machine learning. However, standard methods for probability estimation on discrete objects do not handle object structure in a satisfactory manner. In this paper, we derive a general Bayesian network formulation for probability estimation on leaf-labeled trees that enables flexible approximations which can generalize beyond observations. We show that efficient algorithms for learning Bayesian networks can be easily extended to probability estimation on this challenging structured space. Experiments on both synthetic and real data show that our methods greatly outperform the current practice of using the empirical distribution, as well as a previous effort for probability estimation on trees.
Cheng Zhang (Fred Hutchinson Cancer Research Center)
Erick Matsen IV (Fred Hutchinson Cancer Research Center)
Related Events (a corresponding poster, oral, or spotlight)
2018 Poster: Generalizing Tree Probability Estimation via Bayesian Networks »
Tue Dec 4th through Wed the 5th Room Room 517 AB