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

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 ...

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 ...