Automatic Bayesian curve fitting

doi:10.1111/1467-9868.00128

Automatic Bayesian curve fitting

2002-01-06

338

OA

AI

DOI：10.1111/1467-9868.00128

原文链接

查看原文

摘要

En 中文

A method of estimating a variety of curves by a sequence of piecewise polynomials is proposed, motivated by a Bayesian model and an appropriate summary of the resulting posterior distribution. A joint distribution is set up over both the number and the position of the knots defining the piecewise polynomials. Throughout we use reversible jump Markov chain Monte Carlo methods to compute the posteriors. The methodology has been successful in giving good estimates for 'smooth' functions (i.e. continuous and differentiable) as well as functions which are not differentiable, and perhaps not even continuous, at a finite number of points. The methodology is extended to deal with generalized additive models.

Keyword:

additive models

back-fitting algorithm

least squares regression

piecewise polynomials

reversible jump Markov chain Monte Carlo method

splines

Automatic Bayesian curve fitting

摘要

AI总结

期刊

机构

引用论文