1 citations · 1 across the 2 of their papers we have counts for
3 papers
GPU-Accelerated Sequential Monte Carlo for Bayesian Spectral Analysis
Tomohiro Nabika, Yui Hayashi, Masato Okada
Bayesian spectral deconvolution provides a data-driven framework for mathematical model selection and parameter estimation from spectral data. Although highly versatile, it becomes…
Sequential Exchange Monte Carlo: A Sampling Method for Bayesian Data Analysis without Parameter Tuning
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
Bayesian data analysis is widely used across many disciplines, and representative examples in materials science include spectral analysis and sparse modeling. In such applications,…
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
In this study, we demonstrate a sequential experimental design for spectral measurements by active learning using parametric models as predictors. In spectral measurements, it is n…