Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model

no code implementations • 11 May 2023 • Tomohiro Nabika, Kenji Nagata, Shun Katakami, Masaichiro Mizumaki, Masato Okada

Therefore, we applied Bayesian inference-based data analysis using the exchange Monte Carlo method to realize a sequential experimental design with general parametric models.

Active Learning Bayesian Inference +2

Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)

no code implementations • 11 Dec 2018 • Kenji Nagata, Yoh-ichi Mototake, Rei Muraoka, Takehiko Sasaki, Masato Okada

Since the measurement time is strongly related to the signal-to-noise ratio for the Poisson noise model, Bayesian measurement with Poisson noise model enables us to clarify the relationship between the measurement time and the limit of estimation.

Simultaneous Estimation of Noise Variance and Number of Peaks in Bayesian Spectral Deconvolution

no code implementations • 26 Jul 2016 • 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.

Bayesian Inference