Two- and Multi-dimensional Curve Fitting using Bayesian Inference
Data Analysis, Statistics and Probability
2018-02-23 v2 Instrumentation and Methods for Astrophysics
Statistics Theory
Statistics Theory
Abstract
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.
Cite
@article{arxiv.1802.05339,
title = {Two- and Multi-dimensional Curve Fitting using Bayesian Inference},
author = {Andrew W. Steiner},
journal= {arXiv preprint arXiv:1802.05339},
year = {2018}
}