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統計数学セミナー
過去の記録 ～06/11｜次回の予定｜今後の予定 06/12～
| 担当者 | 吉田朋広、増田弘毅、荻原哲平、小池祐太 |
|---|---|
| 目的 | 確率統計学およびその関連領域に関する研究発表, 研究紹介を行う. |
次回の予定
2026年06月26日(金)
13:30-14:30 数理科学研究科棟(駒場) 122号室
ハイブリッド開催
Prof. Hsin-Hsiung 'Bill’ Huang 氏 (School of Data, Mathematical, and Statistical Sciences, University of Central Florida)
Scalable Bayesian Conformal Inference for High-Dimensional Spatiotemporal Zero-Inflated Count Data (English)
https://u-tokyo-ac-jp.zoom.us/meeting/register/UmviZSskR766wD4QoUvP2g
ハイブリッド開催
Prof. Hsin-Hsiung 'Bill’ Huang 氏 (School of Data, Mathematical, and Statistical Sciences, University of Central Florida)
Scalable Bayesian Conformal Inference for High-Dimensional Spatiotemporal Zero-Inflated Count Data (English)
[ 講演概要 ]
I will present a Bayesian framework for spatiotemporal count data with excess zeros, overdispersion, and ultrahigh-dimensional covariates. The model combines zero-inflated negative binomial regression, TPBN shrinkage priors for sparse fixed effects, graph-Laplacian or SPDE-type spatial random effects, smooth global time effects, and unit-specific Ornstein--Uhlenbeck SDE random effects. Pólya--Gamma augmentation yields a conditionally Gaussian structure, supporting both blocked Gibbs sampling and scalable structured variational inference. I will also discuss split conformal calibration for discrete predictive sets and an auxiliary LAQ/QMLE perspective for the OU component. Simulation studies and a measles surveillance analysis illustrate calibrated prediction and recovery of latent spatiotemporal structure.
[ 参考URL ]I will present a Bayesian framework for spatiotemporal count data with excess zeros, overdispersion, and ultrahigh-dimensional covariates. The model combines zero-inflated negative binomial regression, TPBN shrinkage priors for sparse fixed effects, graph-Laplacian or SPDE-type spatial random effects, smooth global time effects, and unit-specific Ornstein--Uhlenbeck SDE random effects. Pólya--Gamma augmentation yields a conditionally Gaussian structure, supporting both blocked Gibbs sampling and scalable structured variational inference. I will also discuss split conformal calibration for discrete predictive sets and an auxiliary LAQ/QMLE perspective for the OU component. Simulation studies and a measles surveillance analysis illustrate calibrated prediction and recovery of latent spatiotemporal structure.
https://u-tokyo-ac-jp.zoom.us/meeting/register/UmviZSskR766wD4QoUvP2g
