Yuto Miyatake

works on

bayesian inference 1 discretization error 1 ensemble kalman filter 1 ordinary differential equations 1 state-space models 1

From the 1 of 4 linked papers with an AI index.

collaborators

4 papers

math.NA2026

Bayesian Inference of Discretization Error Means in ODEs via Ensemble Kalman Filtering

Shoji Toyota, Yuto Miyatake

The paper introduces a Bayesian method that uses an Ensemble Kalman Filter to estimate the mean of discretization errors in ODE solvers, employing a Markov prior that reflects erro…

math.OC2026

Accelerating SAV-based optimization via randomized low-rank Hessian approximation

Ryo Sagawa, Daisuke Furihata, Yuto Miyatake

We propose a new optimization method, the NystrÃ¶m-enhanced relaxed scalar auxiliary variable method (N-RSAV), which incorporates curvature information into the RSAV framework to a…

math.NA2026

An error control framework for computing the exponential of matrices arising from the finite element discretization

Fuminori Tatsuoka, Yuto Miyatake, Tomohiro Sogabe

Several methods for computing the action of the matrix exponential $e^{A} b$ are expressed by substituting $A$ into a rational appro…

stat.ME2025

Joint Bayesian Inference of Parameter and Discretization Error Uncertainties in ODE Models

Shoji Toyota, Yuto Miyatake

We address the problem of Bayesian inference for parameters in ordinary differential equation (ODE) models based on observational data. Conventional approaches in this setting typi…