Equipped with the Markov assumption, we discuss how to build a Markov chain by estimating conditional probabilities from data. We also discuss the "probabilistic automaton" interpretation of the chain and use it to compute a joint probability.
── Course & channel links ──
Course playlist: Sequence Modeling
https://www.youtube.com/playlist?list=PL2mpR0RYFQsAK40GHyupLV4M-3wH8EOup
── About the author ──
Ben Langmead is a Professor of Computer Science at Johns Hopkins University, where his research spans bioinformatics, computational biology, and data-intensive science. He is the author of Bowtie and Bowtie 2; his group has also developed software like Kraken 2 and resources like recount3 and Index Zone, as well as methods for pangenome indexing and querying, based on e.g. the r-index and move structure. His group's methods have been cited over 130,000 times, and he is the winner of awards including an NSF CAREER award, a Sloan Research Fellowship, the Benjamin Franklin award for contributions to open access, and multiple awards for teaching and mentorship. Ben is the founder and principal of InOrder Labs LLC (https://inorderlabs.com), an expert consulting firm in bioinformatics and computational biology.
Channel: https://www.youtube.com/@BenLangmead
Teaching materials: https://langmead-lab.org/teaching.html
