Bayesian estimate of the degree of a polynomial given a noisy data sample
Statistics Theory
2013-07-18 v1 Probability
Data Analysis, Statistics and Probability
Statistics Theory
Abstract
A widely used method to create a continuous representation of a discrete data-set is regression analysis. When the regression model is not based on a mathematical description of the physics underlying the data, heuristic techniques play a crucial role and the model choice can have a significant impact on the result. In this paper, the problem of identifying the most appropriate model is formulated and solved in terms of Bayesian selection. Besides, probability calculus is the best way to choose among different alternatives. The results obtained are applied to the case of both univariate and bivariate polynomials used as trial solutions of systems of thermodynamic partial differential equations.
Keywords
Cite
@article{arxiv.1307.4602,
title = {Bayesian estimate of the degree of a polynomial given a noisy data sample},
author = {Giovanni Mana and Paolo Alberto Giuliano Albo and Simona Lago},
journal= {arXiv preprint arXiv:1307.4602},
year = {2013}
}
Comments
10 pages, 5 figures, submitted to Metrologia