Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Abstract Background and Objective Medical machine learning (ML) models tend to perform
better on data from the same cohort than on new data, often due to overfitting, or co-variate …

Bayesian statistical modeling has several benefits within an ecological context. In particular,
when observed data are limited in sample size or representativeness, then the Bayesian …

Human Activity Recognition (HAR) has been studied extensively, yet current approaches are
not capable of generalizing across different domains (ie, subjects, devices, or datasets) with …

This introduction recalls the considerable socio-economic challenges associated with
extreme natural hazards. The possibilities of statistical quantification of past hazards and …

Bayesian methods allow borrowing of historical information through prior distributions. The
concept of prior effective sample size (prior ESS) facilitates quantification and …

In statistical process control/monitoring (SPC/M), memory-based control charts aim to detect
small/medium persistent parameter shifts. When a phase I calibration is not feasible, self …

Supplementary Material contains: Appendix A. Proofs. Appendix B. Computational details:
MCMC schema, sampling importance resampling with varying weights. Appendix C. Meta …

The camera is the main sensor of vison-based human activity recognition, and its high-
precision calibration of distortion is an important prerequisite of the task. Current studies …

When using complex Bayesian models to combine information, checking consistency of the
information contributed by different components of the model for inference is good statistical …