A general and basic model for inference about characteristics of a finite population of distinguishable elements is presented from a subjectivistic-Bayesian point of view. A subjectivist analogue to ...

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 76, No. 5 (NOVEMBER 2014), pp. 833-859 (27 pages) The choice of the summary statistics that are used in Bayesian ...

One case often looks very different from the next, and it is precisely this complexity and behavioral variability that makes finding insider threats so tricky. Insider threat actors can cause harm to ...

This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogenous variables appear as regressors. We propose a Limited ...

Bayesian Model Averaging (BMA) provides a coherent mechanism to address the problem of model uncertainty. In this paper we extend the BMA framework to panel data models where the lagged dependent ...

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 ...

Bayesian networks are powerful tools in probabilistic reasoning, allowing us to model complex systems where uncertainty and causal relationships intertwine. At their core, Bayesian Networks are ...

Interventions targeting drinking water safety and responsible dog management could reduce echinococcosis incidence in China.