236 citations · 743 across the 25 of their papers we have counts for
5 papers · 1 filter
{poscosea} : A Computationally Efficient Sensitivity Analysis for Bayesian Models using the posterior covariance representation
Yusaku Ohkubo, Yukito Iba
Bayesian methods are essential in modern data analysis in ecology and evolutionary biology. They provide a flexible framework for modeling complex data-generating processes, while…
Bias correction of posterior means using MCMC outputs
Yukito Iba
We propose algorithms for addressing the bias of the posterior mean when used as an estimator of parameters. These algorithms build upon the recently proposed Bayesian infinitesima…
W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators
Yukito Iba
Evaluating the variability of posterior estimates is a key aspect of Bayesian model assessment. In this study, we focus on the posterior covariance matrix W, defined through the lo…
Posterior covariance information criterion for general loss functions
Yukito Iba, Keisuke Yano
We propose a novel computationally low-cost method for estimating a general predictive measure of generalised Bayesian inference. The proposed method utilises posterior covariance…
Posterior Covariance Information Criterion for Weighted Inference
Yukito Iba, Keisuke Yano
For predictive evaluation based on quasi-posterior distributions, we develop a new information criterion, the posterior covariance information criterion (PCIC. PCIC generalises the…