újraparametrizálást
újraparametrizálást is the process of changing the parameterization of a model by expressing it in terms of new parameters, typically via a differentiable and invertible mapping φ = h(θ). This reparameterization is used to simplify estimation, improve numerical stability, or enforce constraints such as positivity or boundedness.
In estimation and optimization contexts, transformed parameters can improve conditioning, reduce correlations among parameters, and yield
In statistics and Bayesian modeling, reparameterization often targets identifiability and sampling efficiency. A common example is
Common transformations include log, logit, and softmax-like mappings, as well as centered versus non-centered forms in
Caveats include potential loss of interpretability, added complexity in model specification, and the need to ensure