Estimation of Mutual Information for Continuous Variables and its Applications to Graphical Model Construction

2023 Fiscal Year Final Research Report

• Project/Area Number: 18K11192
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 60030:Statistical science-related
• Research Institution: Osaka University
• Principal Investigator: Suzuki Joe 大阪大学, 大学院基礎工学研究科, 教授 (50216397)
• Project Period (FY): 2018-04-01 – 2024-03-31
• Keywords: 相互情報量 / グラフィカルモデル / ベイジアンネットワーク

Outline of Final Research Achievements

This research focuses on the estimation of mutual information in data involving continuous variables and the construction of graphical models. In 2018, we achieved consistency and independence detection in mutual information estimation and presented these results at an international conference. In 2019, we established a method for estimating mutual information in data with continuous variables. In 2020, we proposed a causal discovery method considering confounding factors. In 2021, we developed a method for identifying causal order allowing for the presence of confounders. In 2022, we extended the concept of conditional mutual information. In 2023, we validated the practicality of the proposed methods. These findings have been published in 11 journal articles, including the IEEE Transactions on Information Theory, and presented at four international conferences.

Free Research Field

機械学習の数理

Academic Significance and Societal Importance of the Research Achievements

連続変量を含むデータの相互情報量の正確な推定とグラフィカルモデルの構築を実現し、データ解析や機械学習の分野での新たな手法を提供した。これにより、遺伝子ネットワーク解析や経済データの依存関係解析など、複雑なデータの構造解明が可能となった。また、医療データ解析や金融リスク評価などの実世界の問題解決に応用が期待される。さらに、提案手法の普及を通じて、様々な分野でのデータ活用が促進され、社会全体のデータリテラシー向上にも寄与している。