25 citations · 28 across the 7 of their papers we have counts for
13 papers
Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation
Hayato Okubo, Yoshifumi Amamoto, Toshimitsu Aritake +5
Spectral deconvolution is essential for extracting peak structures that encode material properties and chemical structures, but conventional automated methods often fail when spect…
Unsupervised feature selection using Bayesian Tucker decomposition
Y-h. Taguchi, Yoh-ichi Mototake
In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regression. Although we have propose…
Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI
Yoh-ichi Mototake, Makoto Sasaki
Physics-informed machine learning (PIML) integrates partial differential equations (PDEs) into machine learning models to solve inverse problems, such as estimating coefficient fun…
Algebraic Geometrical Analysis of Metropolis Algorithm When Parameters Are Non-identifiable
Kenji Nagata, Yoh-ichi Mototake
The Metropolis algorithm is one of the Markov chain Monte Carlo (MCMC) methods that realize sampling from the target probability distribution. In this paper, we are concerned with…
Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra
Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura +4
We analyzed the X-ray photoemission spectra (XPS) of carbon 1s states in graphene and oxygen-intercalated graphene grown on SiC(0001) using Bayesian spectroscopy. To realize highly…
Autoregressive with Slack Time Series Model for Forecasting a Partially-Observed Dynamical Time Series
Akifumi Okuno, Yuya Morishita, Yoh-ichi Mototake
This study delves into the domain of dynamical systems, specifically the forecasting of dynamical time series defined through an evolution function. Traditional approaches in this…