Intro to Markov Chains and Bayesian Inference | Mackenzie Simper
About This Video
Markov chains are a special type of random process which can be used to model many natural processes. This workshop will be a gentle introduction to Markov chains, giving basic properties and many examples. The second part of the workshop will focus on one specific application of Markov chains to data science: Sampling from posterior distributions in Bayesian inference. Introductory background in probability, statistics, and linear algebra is assumed.
This workshop was conducted by Mackenzie Simper, PhD Student at Stanford University.
Slides for this workshop: https://bit.ly/markov_chains_ppt
In This Video
PhD Student, Stanford University
Mackenzie is finishing up her PhD at Stanford in mathematics, advised by Persi Diaconis. Her research area is applied probability, and she excited to explore more data science in the future. Outside of research and teaching Mackenzie loves being outside through hiking, biking, and running. Her favorite Markov chain is the overhand shuffle of a deck of cards.