Two Sets of HMM parameters: Emissions, Transitions
16
Example 2-state HMM, observations, scoring, inference
17
Viterbi algorithm: Find best parse π*= argmaxπPx,π
18
Posterior Decoding: Most likely state πiover all paths
19
Summary
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Grab it
Dive into the world of epigenomics and Hidden Markov Models (HMMs) in this comprehensive lecture. Explore the three types of epigenomic modifications, including histone modifications, DNA methylation, and chromatin accessibility. Learn about diverse histone modifications, methylation profiling techniques, and data generation methods like ChIP-Seq. Discover read mapping algorithms, quality control processes, and peak calling techniques. Delve into the foundations of HMMs, including their parameters, parsing, and decoding methods. Understand the Viterbi algorithm for finding the best parse and posterior decoding for determining the most likely state. Gain insights into non-standard modifications, epigenetic inheritance, and developmental memory establishment through engaging Q&A segments.
Epigenomics and Hidden Markov Models in Computational Biology - Lecture 5