Wikipedia says, "A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states." What does that even mean?

In part one of a special two-parter on HMMs, Katie, Ben, and special guest Francesco explain the basics of HMMs, and some simple applications of them in the real world. This episode sets the stage for part two, where we explore the use of HMMs in Modern Genetics, and possibly Katie's "Um Detector."

Introducing Hidden Markov Models (HMM Part 1)

Sςιβrοκεrs Trαdιηg®
https://beta.rstudioconnect.com/content/3091/ryo-eng.html

Introducing Hidden Markov Models (HMM Part 1)

Linear Digressions