Talk 1: Nuts and Bolts of Modern State Space Models - Part I

Scott Linderman; Assistant Professor, Statistics Department at Stanford University Presented March 28, 2023
Talk 1 Overview: State space models are fundamental tools for analyzing sequential data like neural and behavioral time series. These tools offer a lens into the latent states and dynamics underlying high-dimensional measurements. In the first lecture, I will cover the foundations of probabilistic state space modeling, assuming little background aside from linear algebra, multivariate calculus, and basic probability. We will cover discrete and continuous state space models like Hidden Markov Models and linear Gaussian dynamical systems, as well as more complex models like switching linear and nonlinear dynamical systems. I will discuss both exact and approximate algorithms for learning (i.e., parameter estimation) and inference (i.e., state estimation). We will intersperse mathematical derivations with code demos using the new dynamax library.

[@yorailevi6747]

Really interesting! Thanks!