9 citations · 11 across the 7 of their papers we have counts for
4 papers · 1 filter
Fast and Locally Adaptive Bayesian Quantile Smoothing using Calibrated Variational Approximations
Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa
Quantiles are useful characteristics of random variables that can provide substantial information on distributions compared with commonly used summary statistics such as means. In…
Approximate Gibbs sampler for Bayesian Huberized lasso
Jun Kawakami, Shintaro Hashimoto
The Bayesian lasso is well-known as a Bayesian alternative for Lasso. Although the advantage of the Bayesian lasso is capable of full probabilistic uncertain quantification for par…
Sparse Bayesian inference on gamma-distributed observations using shape-scale inverse-gamma mixtures
Yasuyuki Hamura, Takahiro Onizuka, Shintaro Hashimoto +1
In various applications, we deal with high-dimensional positive-valued data that often exhibits sparsity. This paper develops a new class of continuous global-local shrinkage prior…
Locally Adaptive Spatial Quantile Smoothing: Application to Monitoring Crime Density in Tokyo
Takahiro Onizuka, Shintaro Hashimoto, Shonosuke Sugasawa
Spatial trend estimation under potential heterogeneity is an important problem to extract spatial characteristics and hazards such as criminal activity. By focusing on quantiles, w…