Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019 - Bayesian Networks 2 - Forward-Backward | Stanford CS221: AI (Autumn 2019)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2ZszFms Topics: Bayesian Networks Percy Liang, Associate Professor & Dorsa Sadigh, Assistant Professor - Stanford University http://onlinehub.stanford.edu/ Associate Professor Percy Liang Associate Professor of Computer Science and Statistics (courtesy) https://profiles.stanford.edu/percy-liang Assistant Professor Dorsa Sadigh Assistant Professor in the Computer Science Department & Electrical Engineering Department https://profiles.stanford.edu/dorsa-sadigh To follow along with the course schedule and syllabus, visit: https://stanford-cs221.github.io/autumn2019/#schedule 0:00 Introduction 0:23 Review: Bayesian network 1:58 Review: probabilistic inference 8:48 Hidden Markov model inference 31:12 Lattice representation 34:16 Summary 35:38 Hidden Markov models 41:04 Review: beam search 44:49 Step 1: propose 46:04 weight 50:13 Step 3: resample 56:01 Application: object tracking 57:24 Particle filtering demo 59:36 Roadmap 59:39 Gibbs sampling