From the 1 of 14 papers with an AI index.
3 citations
- Nagoya UniversityJP6 papers
- Kavli Institute for the Physics and Mathematics of the UniverseJP4 papers
- National Astronomical Observatory of JapanJP4 papers
- The Graduate University for Advanced Studies, SOKENDAIJP4 papers
- California Institute of TechnologyUS3 papers
- Tohoku UniversityJP3 papers
- Aalto UniversityFI2 papers
- Academy of AthensGR2 papers
- Albert Einstein College of MedicineUS2 papers
- Applied Mathematics (United States)US2 papers
- Astronomy and SpaceAU2 papers
- Boston UniversityUS2 papers
14 papers
Maximum Likelihood and Bayesian Estimation for State-Space Models Using the Non-Gaussian Filter
Genshiro Kitagawa
The paper revisits deterministic non‑Gaussian filtering for nonlinear state‑space models, showing it can be used effectively for maximum likelihood and Bayesian estimation thanks t…
Symmetric Quantum Walks on Hamming Graphs and Their Limit Distributions
Robert Griffiths, Shuhei Mano
We study a class of symmetric coined quantum walks on Hamming graphs, where the distance between vertices specifies the transition probability. A special model is the simple quantu…
Revisiting Marked Galaxy Clustering from a Joint Point Process Perspective
Tsutomu T. Takeuchi
Marked correlation functions, in which galaxy properties such as luminosity or stellar mass are treated as marks, are widely used to test models of galaxy formation. In astronomy,…
Sub-exponential Growth Dynamics in Complex Systems: A Piecewise Power-Law Model for the Diffusion of New Words and Names
Hayafumi Watanabe
The diffusion of ideas and language in society has conventionally been described by S-shaped models, such as the logistic curve. However, the role of sub-exponential growth -- a sl…
TabPFN Extensions for Interpretable Geotechnical Modelling
Taiga Saito, Yu Otake, Daijiro Mizutani +1
Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where uncertainty quantification and interpretability matter as much as predictive accuracy. We ev…
Denoising weak lensing mass maps with diffusion model: systematic comparison with generative adversarial network
Shohei D. Aoyama, Ken Osato, Masato Shirasaki
Removing the shape noise from the observed weak lensing field, i.e., denoising, enhances the potential of WL by accessing information at small scales where the shape noise dominate…