A stochastic approximation algorithm is proposed for recursive estimation of the hyperparameters characterizing, in a population, the probability density function of ...

We consider situations in Bayesian analysis where the prior is indexed by a hyperparameter taking on a continuum of values. We distinguish some arbitrary value of the hyperparameter, and consider the ...

In the realm of machine learning, the performance of a model often hinges on the optimal selection of hyperparameters. These parameters, which lie beyond the control of the learning algorithm, dictate ...