metaparameters

Metaparameters are quantities that govern the structure or learning process used to determine a model's parameters. They do not directly encode information about the data; rather, they influence how the parameters are estimated, regularized, or initialized.

The term is used differently across disciplines. In many statistical contexts, metaparameters are synonymous with hyperparameters, especially those that specify priors or hierarchical structure (hyperpriors). In machine learning, metaparameters often refer to settings that control learning dynamics or architecture, such as learning rate, regularization strength, and the number of layers.

Practically, metaparameters are chosen by the modeller and may be tuned via cross-validation, held-out performance, or Bayesian optimization. In Bayesian inference, one can place priors on metaparameters and infer them jointly with model parameters, though this increases computational load.

The choice of metaparameters affects convergence, bias, and generalization. Poorly chosen values can lead to slow learning or overfitting. Automated methods—grid or random search, Bayesian optimization, or gradient-based approaches—are commonly used to locate effective ranges.

Metaparameters lie at the intersection of model specification and learning strategy, and understanding their role helps clarify how models balance flexibility, prior information, and computational feasibility.