Exploring Hidden Markov Models | Julia Christina Costacurta
About This Video
Exploring Hidden Markov Models | Julia Christina Costacurta
Hidden Markov Models (HMMs) are used to describe and analyze sequential data in a wide range of fields, including handwriting recognition, protein folding, and computational finance. In this workshop, we will cover the basics of how HMMs are defined, why we might want to use one, and how to implement an HMM in Python. This workshop might be of particular interest to attendees from May 25’s “Intro to Markov Chains and Bayesian Inference” session. Introductory background in probability, statistics, and linear algebra is assumed.
This workshop was conducted by Julia Christina Costacurta, PhD Candidate at Stanford University
Useful resources for this workshop:
– https://bit.ly/hmm_presentation
– https://bit.ly/hmm_tutorial_notebook
In This Video
PhD Candidate, Stanford University
Julia is a PhD candidate in Electrical Engineering at Stanford, advised by Scott Linderman. Her research area is computational neuroscience, and she’s specifically curious about the ways that statistical models can help us understand and uncover structure in the rich datasets present in neuroscience. Outside of research and teaching Julia enjoys reading, sewing, and rock climbing.