This course equips learners with the theoretical knowledge and computational skills needed to implement modern Bayesian statistical methods in real-world settings. By completing the course, learners ...

Approximate Bayesian computation (ABC) constitutes a family of likelihood-free methods that have emerged as a cornerstone in statistical inference for complex models where evaluation of the likelihood ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

On Wednesday the 1st of April 2026, M.Eng. Chengkun Li defends his PhD thesis on Surrogate-based methods for efficient Bayesian posterior computation. The thesis is related to research done in the ...

In recent years, the frequency of weather-related natural disasters—cyclones, torrential rains, floods—has increased as a ...

Black-box optimization, particularly Bayesian optimization, is a practical approach for weather-intervention design, achieving meaningful rainfall ...