Learning to Count -- Bayesian Inference in Science and Engineering

Dr. Curtis Smith
Distinguished Staff Engineer
Idaho National Laboratory

By exploring how past scientists have made observations when counting events, we will trace a path to modern applications of Bayesian inference. Starting at the late 1700s, we will see how early probabilistic ideas supported fundamental science such as celestial mechanics. Viewed through the activities of Pierre-Simon Laplace, we will begin to understand how current inference on Poisson and Bernoulli processes is performed by reviewing the first Bayesian counting application.