varyb
Varyb is a term used in statistics and machine learning to describe a family of methods that explicitly model and adapt to variability in data. In this context, "vary" suggests changing conditions across observations, while the suffix "b" is used to evoke Bayesian-style inference. Varyb methods aim to produce predictions that reflect uncertainty not only in the mean relationship between inputs and outputs but also in the variance around that relationship.
Origin and scope: The concept emerged in theoretical discussions during the late 2010s and has been described
Methodology: A common implementation builds a joint model for the target y and a noise scale parameter
Applications: Varyb ideas are used in time-series forecasting, sensor networks, finance, and genomics, where accounting for
Variants and limitations: Several variants exist, such as lightweight versions for low-resource environments and ensemble approaches.
See also: Heteroscedasticity, Bayesian regression, Gaussian processes.