| StanとRでベイズ統計モデリング (Wonderful R 2) 松浦健太郎 共立出版, 2016 | 74* | 2016 |
| An assessment of self-reported COVID-19 related symptoms of 227,898 users of a social networking service in Japan: Has the regional risk changed after the declaration of the … S Nomura, D Yoneoka, S Shi, Y Tanoue, T Kawashima, A Eguchi, ... The Lancet Regional Health–Western Pacific 1, 2020 | 46 | 2020 |
| Good arm identification via bandit feedback H Kano, J Honda, K Sakamaki, K Matsuura, A Nakamura, M Sugiyama Machine Learning 108, 721-745, 2019 | 35 | 2019 |
| Bayesian Statistical Modeling with Stan, R, and Python K Matsuura Springer, Singapore, 2023 | 5 | 2023 |
| ベイズ流決定理論を用いる臨床試験: 効用とサンプルサイズ設計 坂巻顕太郎， 兼清道雄， 大和田章一， 松浦健太郎， 柿爪智行， 高橋文博， ... 計量生物学 41 (1), 55-91, 2020 | 2 | 2020 |
| Optimal dose escalation methods using deep reinforcement learning in phase I oncology trials K Matsuura, K Sakamaki, J Honda, T Sozu Journal of biopharmaceutical statistics 33 (5), 639-652, 2023 | 1 | 2023 |
| Optimal adaptive allocation using deep reinforcement learning in a dose‐response study K Matsuura, J Honda, I El Hanafi, T Sozu, K Sakamaki Statistics in Medicine 41 (7), 1157-1171, 2022 | 1 | 2022 |
| Matrix decomposition in meta‐analysis for extraction of adverse event pattern and patient‐level safety profile K Matsuura, J Tsuchida, S Ando, T Sozu Pharmaceutical Statistics 20 (4), 806-819, 2021 | 1 | 2021 |
