Bayesian inference has emerged as a powerful tool in the analysis of queueing systems, blending probability theory with statistical estimation to update beliefs about system parameters as new data ...

Probabilistic Graphical Models (PGMs) are a popular way of portraying condi- tional dependencies between random variables (randvars) of a complex proba- bility distribution. One of the main purposes ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

Bayesian networks, also known as Bayes nets, belief networks, or decision networks, are a powerful tool for understanding and reasoning about complex systems under uncertainty. They are essentially ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...