Two- and Multi-dimensional Curve Fitting using Bayesian Inference

Understand

Fitting models to data using Bayesian inference is quite common, but when each point in parameter space gives a curve, fitting the curve to a data set requires new nuisance parameters, which specify the metric embedding the one-dimensional curve into the higher-dimensional space occupied by the data.

• A generic formalism for curve fitting in the context of Bayesian inference is developed which shows how the aforementioned metric arises.
• The result is a natural generalization of previous works, and is compared to oft-used frequentist approaches and similar Bayesian techniques.