Topic: Bayesian data analysis

Bayesian data analysis using probabilistic modeling explicitly utilizes one's a priori scientific beliefs as structured prior distributions. However, scientific inference often requires some of our prior knowledge only be embedded in a `broad sense', take for example inferring latent dynamics underlying neural population activity; nonparametric approaches such as Gaussian Processes (GPs) are highly flexible, expressive, and allow us to encode broad assumptions such as periodicity, stationarity, and smoothness. Though, these properties make GPs attractive for neural data analysis, their comput