44 citations · 84 across the 18 of their papers we have counts for
5 papers · 1 filter
The Group R2D2 Shrinkage Prior for Sparse Linear Models with Grouped Covariates
Eric Yanchenko, Kaoru Irie, Shonosuke Sugasawa
Shrinkage priors are a popular Bayesian paradigm to handle sparsity in high-dimensional regression. Still limited, however, is a flexible class of shrinkage priors to handle groupe…
Quantifying uncertainty in the numerical integration of evolution equations based on Bayesian isotonic regression
Yuto Miyatake, Kaoru Irie, Takeru Matsuda
This paper presents a new Bayesian framework for quantifying discretization errors in numerical solutions of ordinary differential equations. By modelling the errors as random vari…
On the Proofs of the Predictive Synthesis Formula
Riku Masuda, Kaoru Irie
Bayesian predictive synthesis is useful in synthesizing multiple predictive distributions coherently. However, the proof for the fundamental equation of the synthesized predictive…
State-Space Modeling of Shape-constrained Functional Time Series
Daichi Hiraki, Yasuyuki Hamura, Kaoru Irie +1
Functional time series data frequently appears in econometric analyses, where the functions of interest are subject to some shape constraints, including monotonicity and convexity,…
Zero-inflated stochastic volatility model for disaggregated inflation data with exact zeros
Geonhee Han, Kaoru Irie
The disaggregated time-series for the Consumer Price Index (CPI) often exhibits exact zero price changes, stemming from structural features of the data collection process. However,…