Gibbs Sampling is a statistical technique used to generate samples from a complex probability distribution. It works by breaking down the distribution into simpler, conditional distributions. By iteratively sampling from these conditional distributions, we can approximate the overall distribution, even if it’s difficult to sample from directly. This method is particularly useful in Bayesian statistics and machine learning, where we often deal with high-dimensional data. By focusing on one variable at a time while keeping others fixed, Gibbs Sampling helps us explore the space of possible outcomes, making it easier to understand complex relationships in the data.