Bayesian Inference for Experimental Data
Bayesian hierarchical modeling and MCMC for uncertainty quantification in experimental data.
Bayesian hierarchical modeling and MCMC (Metropolis–Hastings) used to estimate posterior distributions and quantify uncertainty — including modeling pupillary response to verb frequency in a psycholinguistics study.
Tools: Python (PyMC, NumPy), Bayesian statistics.