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 ...

We develop novel methods to make Bayesian inference more efficient, scalable, and practical. This includes work on variational methods, Monte Carlo algorithms, and techniques for handling complex ...

Hiranya Peiris holds the Professorship of Astrophysics (1909) at the University of Cambridge and is a member of the Kavli Institute for Cosmology. Her research centers on extracting fundamental ...