Bayesian quantile regression and statistical modelling represent a growing paradigm in contemporary data analysis, extending conventional regression by estimating various conditional quantiles rather ...

Aim: Our aim was to develop predictive statistical models for mapping the abundance of 18 waterfowl species at a pan-Canadian level. We refined the previous generation of national waterfowl models by ...

This course covers the ideas underlying statistical modelling in science through the lens of causal thinking. We cover the implementation of these ideas through Bayesian computational methods and ...

A research team introduces a hierarchical Bayesian spatial approach that integrates UAV and terrestrial LiDAR data to estimate AGB of individual trees in natural secondary forests of northeastern ...

Exchangeability of observations corresponds to a condition shared by the vast majority of applications of the Bayesian paradigm. By de Finetti's representation theorem, if exchangeable observations ...

This study examined the relationship between the Monetary Policy Rate (MPR) and inflation across five continents from 2014 to 2023 using both Frequentist and Bayesian Linear Mixed Models (LMM). It ...

Machine Learning gets all the marketing hype, but are we overlooking Bayesian Networks? Here's a deeper look at why "Bayes Nets" are underrated - especially when it comes to addressing probability and ...

Study Design Descriptive laboratory study. Objectives Use descriptive subject data to predict specific movement strategies in subjects with chronic ankle instability. Background Subjects with chronic ...

What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better ...