Bioinformatics
The Metropolis-Hastings Algorithm is a Markov Chain Monte Carlo (MCMC) method used for obtaining a sequence of random samples from a probability distribution when direct sampling is difficult. This algorithm is particularly valuable in Bayesian inference as it allows for the estimation of posterior distributions by generating samples that approximate these distributions, making it easier to draw inferences about parameters of interest.
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