Hidden Markov Models (HMMs) represent a probabilistic framework within bioinformatics that models systems with unobserved (hidden) states generating observable sequences via specific transition and emission probabilities, governed by the Markov property where future state dependence is strictly limited to the immediately preceding state. This theory formalizes multiple sequence alignment by defining likelihood functions over all possible paths of insertion, deletion, and match states, utilizing Bayesian principles to optimize model parameters through maximum likelihood estimation for identifying evolutionary conservation domains. The concept bridges stochastic process theory with computational biology, providing a rigorous mechanism for inferring ancestral sequences and functional relationships from divergent observed data without direct knowledge of intermediate historical states.
Hidden Markov Models (HMMs) represent a probabilistic framework within bioinformatics that models systems with unobserved (hidden) states generating observable sequences via specific transition and e…