52 citations · 98 across the 17 of their papers we have counts for
17 papers
Automatic Termination Strategy of Inelastic Neutron-scattering Measurement Using Bayesian Optimization for Bin-width Selection
Kensuke Muto, Hirotaka Sakamoto, Kenji Nagata +2
Currently, an excessive amount of event data is being obtained in four-dimensional inelastic neutron-scattering experiments. A method for automatic bin-width optimization of multid…
Mesoscopic Bayesian Inference by Solvable Models
Shun Katakami, Shuhei Kashiwamura, Kenji Nagata +2
The rapid advancement of data science and artificial intelligence has affected physics in numerous ways, including the application of Bayesian inference, setting the stage for a re…
Bayesian Inference for Small-Angle Scattering Data II: Core-Shell Samples
Keigo Oyama, Yui Hayashi, Shigeo Kuwamoto +4
Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays, are employed in various scientific fields, including materials science, biochemistry, and polymer physic…
Basis Function Dependence of Estimation Precision for Synchrotron-Radiation-Based Mössbauer Spectroscopy
Binsheu Shieh, Ryo Masuda, Satoshi Tsutsui +4
Mössbauer spectroscopy is a technique employed to investigate the microscopic properties of materials using transitions between energy levels in the nuclei. Conventionally, in sync…
Quantitative Selection of Sample Structures in Small-Angle Scattering Using Bayesian Methods
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
Small-angle scattering (SAS) is a key experimental technique for analyzing nano-scale structures in various materials.In SAS data analysis, selecting an appropriate mathematical mo…
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 for Small-Angle Scattering Data
Yui Hayashi, Shun Katakami, Shigeo Kuwamoto +3
In this paper, we propose a method for estimating model parameters using Small-Angle Scattering (SAS) data based on the Bayesian inference. Conventional SAS data analyses involve p…
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…
Bayesian Inference on Hamiltonian Selections for Mössbauer Spectroscopy
Ryota Moriguchi, Satoshi Tsutsui, Shun Katakami +3
Mössbauer spectroscopy, which provides knowledge related to electronic states in materials, has been applied to various fields such as condensed matter physics and material science…
Bayesian Spectral Deconvolution of X-Ray Absorption Near Edge Structure Discriminating High- and Low-Energy Domains
Shuhei Kashiwamura, Shun Katakami, Ryo Yamagami +6
In this paper, we propose a Bayesian spectral deconvolution considering the properties of peaks in different energy domains. Bayesian spectral deconvolution regresses spectral data…
Fast Bayesian Deconvolution using Simple Reversible Jump Moves
Koki Okajima, Kenji Nagata, Masato Okada
We propose a Markov chain Monte Carlo-based deconvolution method designed to estimate the number of peaks in spectral data, along with the optimal parameters of each radial basis f…
Intrinsic regularization effect in Bayesian nonlinear regression scaled by observed data
Satoru Tokuda, Kenji Nagata, Masato Okada
Occam's razor is a guiding principle that models should be simple enough to describe observed data. While Bayesian model selection (BMS) embodies it by the intrinsic regularization…
Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)
Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka +2
In this paper, we propose a new method of Bayesian measurement for spectral deconvolution, which regresses spectral data into the sum of unimodal basis function such as Gaussian or…
Simultaneous Estimation of Noise Variance and Number of Peaks in Bayesian Spectral Deconvolution
Satoru Tokuda, Kenji Nagata, Masato Okada
The heuristic identification of peaks from noisy complex spectra often leads to misunderstanding of the physical and chemical properties of matter. In this paper, we propose a fram…
Oscillations in Spurious States of the Associative Memory Model with Synaptic Depression
Shin Murata, Yosuke Otsubo, Kenji Nagata +1
The associative memory model is a typical neural network model, which can store discretely distributed fixed-point attractors as memory patterns. When the network stores the memory…
Influence of synaptic depression on memory storage capacity
Yosuke Otsubo, Kenji Nagata, Masafumi Oizumi +1
Synaptic efficacy between neurons is known to change within a short time scale dynamically. Neurophysiological experiments show that high-frequency presynaptic inputs decrease syna…
Instabilities in associative memory model with synaptic depression and switching phenomena among attractors
Yosuke Otsubo, Kenji Nagata, Masafumi Oizumi +1
We investigated how the stability of macroscopic states in the associative memory model is affected by synaptic depression. To this model, we applied the dynamical mean-field theor…