👉 Bay computing, also known as Bayesian computation, is a statistical framework that leverages Bayes' theorem to model and infer probabilistic relationships within complex systems. It involves updating prior beliefs about parameters or hypotheses with observed data to obtain posterior distributions, which encapsulate updated knowledge and uncertainty. This approach is particularly powerful in scenarios with limited data or high noise, as it naturally incorporates prior information and quantifies uncertainty explicitly. By iteratively refining these beliefs through the lens of likelihood functions, Bay computing enables robust inference and decision-making in fields ranging from machine learning to bioinformatics, where understanding probabilistic dependencies is crucial.
