The sklearn.neural_network.BernoulliRBM.gibbs function is a method in Python's scikit-learn library that performs Gibbs sampling, a technique used for sampling from a probability distribution defined over discrete variables. In the context of the Bernoulli Restricted Boltzmann Machine (RBM), this method is used to generate a new sample from the learned joint distribution of the visible and hidden units. It iteratively updates the values of the hidden and visible units by sampling from their conditional distributions, based on the current values of the other units. The number of Gibbs sampling steps can be specified as a parameter to control the convergence and quality of the generated samples.
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