18 citations · 18 across the 5 of their papers we have counts for
4 papers · 1 filter
Fast covariance-free spatiotemporal modeling via coarse-to-fine learning
Daisuke Murakami
Scalable spatiotemporal modeling remains challenging because conventional methods rely on covariance models that tightly couple spatial representation and temporal inference, often…
Scalable coarse-to-fine spatial downscaling
Daisuke Murakami, Yongwan Chun, Takahiro Yoshida +1
This study proposes coarse-to-fine downscaling (CF-DS), a scalable spatial downscaling method extending coarse-to-fine spatial modeling. Unlike conventional spatial-statistical dow…
Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
We develop a coarse-to-fine generalized linear mixed model (CF-GLMM) for scalable spatial prediction of exponential-family responses. CF-GLMM extends coarse-to-fine spatial modelin…
Coarse-to-fine spatial modeling: A scalable, machine-learning-compatible spatial model
Daisuke Murakami, Alexis Comber, Takahiro Yoshida +3
This study proposes coarse-to-fine spatial modeling (CFSM) as a scalable and machine learning-compatible alternative to conventional spatial process models. Unlike conventional cov…