Arkadiusz Wiśniowski

TL;DR: The utility of three complementary methods for predicting hospital length of stay (LoS) using UK national- and hospital-level data is demonstrated and data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

TL;DR: A Bayesian model is proposed to overcome the limitations of the various data sources and produces a synthetic database with measures of uncertainty for international migration flows and other model parameters from 2002 to 2008.

TL;DR: A fully integrated and dynamic Bayesian approach to forecast populations by age and sex is developed and compared to different forecast models for age-specific fertility, mortality, and migration.

TL;DR: In this paper, the authors present Bayesian forecasts of immigration for seven European countries to 2025, based on quantitative data and qualitative knowledge elicited from country-specific migration experts in a two-round Delphi survey.

TL;DR: This work proposes a method of combining both probability and nonprobability samples in a way that exploits their strengths to overcome their weaknesses within a Bayesian inferential framework, and demonstrates that informative priors based on nonProbability data can lead to reductions in variances and mean squared errors for linear model coefficients.