Information-theoretic bounds on learning network dynamics
published: March 7, 2016, recorded: December 2015, views: 57
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
How long should we observe the trajectory of a system before being able to characterize its underlying network dynamics? I will present a brief review of information-theoretic tools to establish lower bounds on the required length of observation. I will illustrate the use of these tools with a few examples: linear and nonlinear stochastic differential equations, dynamical Bayesian networks and so on. For each of these examples, I will discuss whether the ultimate information limit has been achieved by practical algorithms or not.
Download slides: netadis2015_montanari_network_dynamics_01.pdf (279.2 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !