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Bibliographic Details
Main Author: Scargle, Jeffrey D.
Format: Preprint
Published: 2001
Subjects:
Online Access:https://arxiv.org/abs/math/0111127
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author Scargle, Jeffrey D.
author_facet Scargle, Jeffrey D.
contents This paper derives practical algorithms, based on Bayesian inference methods, for several data analysis problems common in time series analysis of astronomical and other data. One problem is the determination of the lag between two time series, for which the cross-correlation function is a sufficient statistic. The second problem is the estimation of structure in a time series of measurements which are a weighted integral over a finite range of the independent variable.
format Preprint
id arxiv_https___arxiv_org_abs_math_0111127
institution arXiv
publishDate 2001
record_format arxiv
spellingShingle Bayesian Estimation of Time Series Lags and Structure
Scargle, Jeffrey D.
Numerical Analysis
Probability
This paper derives practical algorithms, based on Bayesian inference methods, for several data analysis problems common in time series analysis of astronomical and other data. One problem is the determination of the lag between two time series, for which the cross-correlation function is a sufficient statistic. The second problem is the estimation of structure in a time series of measurements which are a weighted integral over a finite range of the independent variable.
title Bayesian Estimation of Time Series Lags and Structure
topic Numerical Analysis
Probability
url https://arxiv.org/abs/math/0111127