1 citations · 1 across the 3 of their papers we have counts for
4 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…
Bayesian Inference of Absorption Spectra Based on Binomial Distribution
Tomohiro Nabika, Kenji Nagata, Shun Katakami +2
In this paper, we propose a Bayesian spectral deconvolution method for absorption spectra. In conventional analysis, the noise mechanism of absorption spectral data is never consid…