Computational Biology
Gibbs sampling is a Markov Chain Monte Carlo (MCMC) algorithm used to generate samples from a multivariate probability distribution when direct sampling is difficult. This method iteratively samples from the conditional distributions of each variable, allowing for efficient exploration of complex high-dimensional spaces. It plays a critical role in tasks such as motif discovery and regulatory element identification, where understanding the underlying probability distributions is essential for accurate modeling.
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