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Bayesian Statistics and Uncertainty Quantification Lab (Satoru Tokuda)
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A lab that uses Bayesian statistics to understand physical phenomena from measurement data without ambiguity.
AboutAbout the lab
Interdisciplinary statistics linking the ideal and the real.
The Tokuda Lab aims to establish guiding principles for understanding physical phenomena from measurement data without ambiguity.
The lab combines mathematical modeling, which represents data with simple equations, and statistical inference, which selects models in light of the data.
While exploring the mathematics of Bayesian statistics as the foundation, the lab also conducts joint research with researchers in various sciences and in industry.
The lab is recruiting fourth-year students from the Department of Mathematics, Faculty of Science who wish to join, as well as incoming graduate students of the Graduate School of Mathematics and the Joint Graduate School of Mathematics for Innovation.
Key terms
- Bayesian statistics
- A statistical approach that quantifies, as probabilities, how plausible each model is given the data.
- Mathematical modeling
- A method of representing measurement data with simple equations in order to separate the universal and individual aspects of a phenomenon and understand them.
- Model selection
- The task of choosing, from multiple candidates, the model that best matches reality by quantitatively comparing them with measurement data.
- Dirac electrons
- Electrons in solids that are theoretically predicted to behave like massless particles.
NewsLab news
View More ↗ResearchResearch themes
This lab aims to establish "modeling principles" for understanding physical phenomena from measurement data without ambiguity, and combines mathematical modeling with statistical inference to do so. It focuses on three challenges, namely model indeterminacy, the effect of observation noise, and the effect of model mismatch, and addresses them through both theory and empirical work.
*Illustrative imageModel Indeterminacy
What is essential to a phenomenon is not self-evident, and researchers sometimes disagree. Using Bayesian statistics, which expresses the plausibility of each model as a probability, the lab works on model selection for things such as velocity distribution functions and band structures.
*Illustrative imageEffect of Observation Noise
Measurement data come with observation noise, and the larger the noise, the more uncertain the estimated parameters become. The lab develops methods to estimate the noise variance and an appropriate model simultaneously, and is also pioneering asymptotic theory for Bayesian estimation.
*Illustrative imageEffect of Model Mismatch
Besides random observation noise, there are discrepancies such as systematic errors between a model and measurement data, which are collectively called model mismatch. The lab explores systematic ways to handle it, along with new theory to support their validity.
*Illustrative imageApplication to Understanding Complex Phenomena
As an example of joint research, the lab proposed a method for modeling angle-resolved photoemission spectroscopy data with Bayesian statistics. Applied to the topological material TlBi(S,Se)2, it contributed to determining the mass of Dirac electrons.
MembersFaculty
The members of the Tokuda Lab consist of faculty, part-time lecturers, technical staff, and master's and undergraduate students.As of 2026
- Associate ProfessorSatoru Tokuda
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Taking measurements” Deo photographer / CC BY-SA 4.0 (cropped)
- Model Indeterminacy: “Trees in fog on the northern side of Loch Tay, Scottish Highlands, Scotland” Michal Klajban / CC BY-SA 4.0 (cropped)
- Effect of Observation Noise: “Oscilloscope with noise ( )” Jean-Etienne Minh-Duy Poirrier from USA / CC BY-SA 2.0 (cropped)
- Effect of Model Mismatch: “Comsol simulation of grounding rods in physical simulator Comsol” Powerfox / CC BY-SA 4.0 (cropped)
- Application to Understanding Complex Phenomena: “CollageFisica” Daniele Pugliesi / Public domain (cropped)
- Related lab (〈Bayesian Statistics / Uncertainty Quantification〉): “VLT 4LGSF OTA L2 lens measurement on NANOMEFOS (tno kamphues 22)” F. Kamphues/TNO/ESO / CC BY 4.0 (cropped)
- Related lab (〈Statistical Science / Small Area Estimation〉): “Colorado census statistical areas 2020” Buaidh / CC0 (cropped)
- Related lab (Statistical Science and Small Area Estimation Lab (Masayo Hirose)): “1922 Index of Great Trigonometrical Survey of India” Survey of India, Government of India / Public domain (cropped)
- Related lab (Statistical Analysis and Sparse Estimation Lab): “Factor rotatie” Frederik Beuk / Public domain (cropped)
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