20 citations · 68 across the 28 of their papers we have counts for
6 papers · 1 filter
Adjoint-based exact Hessian computation
Shin-ichi Ito, Takeru Matsuda, Yuto Miyatake
We consider a scalar function depending on a numerical solution of an initial value problem, and its second-derivative (Hessian) matrix for the initial value. The need to extract t…
Estimation of ordinary differential equation models with discretization error quantification
Takeru Matsuda, Yuto Miyatake
We consider parameter estimation of ordinary differential equation (ODE) models from noisy observations. For this problem, one conventional approach is to fit numerical solutions (…
Information criteria for non-normalized models
Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen
Many statistical models are given in the form of non-normalized densities with an intractable normalization constant. Since maximum likelihood estimation is computationally intensi…
Predictive density estimation under the Wasserstein loss
Takeru Matsuda, William E. Strawderman
We investigate predictive density estimation under the Wasserstein loss for location families and location-scale families. We show that plug-in densities form a complete clas…
Imputation estimators for unnormalized models with missing data
Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim
Several statistical models are given in the form of unnormalized densities, and calculation of the normalization constant is intractable. We propose estimation methods for such unn…
Unified estimation framework for unnormalized models with statistical efficiency
Masatoshi Uehara, Takafumi Kanamori, Takashi Takenouchi +1
The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing c…