Sally Paganin

Sally Paganin works in statistics and computational methods, focusing on Bayesian approaches (a framework for reasoning about uncertainty using probability) to clustering, latent variable modeling, and item response theory (methods for analyzing survey or test data). Her work often addresses how to build computationally efficient algorithms, such as Markov Chain Monte Carlo (MCMC) simulation methods, and how to design flexible statistical models for grouping data points (clustering) or analyzing brain imaging and psychological measurement data.

Publication output has grown from occasional papers earlier in the decade to a steadier pace of 3-5 per year since 2021, suggesting an increasing and consistent research output in recent years.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026