Importance Sampling

The machine learning consultancy: https://truetheta.io Join my email list to get educational and useful articles (and nothing else!): https://mailchi.mp/truetheta/true-theta-email-list Want to work together? See here: https://truetheta.io/about/#want-to-work-together Calculating expectations is frequent task in Machine Learning. Monte Carlo methods are some of our most effective approaches to this problem, but they can suffer from high variance estimates. Importance Sampling is a clever technique to obtain lower variance estimates. SOCIAL MEDIA LinkedIn : https://www.linkedin.com/in/dj-rich-90b91753/ Twitter : https://twitter.com/DuaneJRich Github: https://github.com/Duane321 Enjoy learning this way? Want me to make more videos? Consider supporting me on Patreon: https://www.patreon.com/MutualInformation SOURCES [1] was my primary source. Chapter 17 of [2] and chapter 23 of [3] provided a useful discussion more directed at the use cases of Machine Learning. ----------------------------- [1] E. Anderson, "Monte Carlo Methods and Importance Sampling", https://ib.berkeley.edu/labs/slatkin/eriq/classes/guest_lect/mc_lecture_notes.pdf [2] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016 [3] K. P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press, 2012 TIMESTAMP 0:00 Intro 0:16 Monte Carlo Methods 2:29 Monte Carlo Example 3:57 Distribution of Monte Carlo Estimate 6:06 Importance Sampling 9:00 Importance Sampling Example 11:40 When to use Importance Sampling