Batch Updating of a Posterior Tree Distribution over a Meta-Tree
arXiv:2303.09705v24 citationsh-index: 14
Originality Synthesis-oriented
AI Analysis
This is an incremental improvement for researchers in probabilistic modeling and tree-based methods, focusing on computational efficiency rather than a new application.
The paper tackles the problem of efficiently updating a posterior distribution over a set of trees (meta-tree) in a probabilistic data generation model, proposing a batch updating method to improve computational efficiency.
Previously, we proposed a probabilistic data generation model represented by an unobservable tree and a sequential updating method to calculate a posterior distribution over a set of trees. The set is called a meta-tree. In this paper, we propose a more efficient batch updating method.