𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A Bayesian approach to detecting change points in climatic records

✍ Scribed by Eric Ruggieri


Publisher
John Wiley and Sons
Year
2012
Tongue
English
Weight
267 KB
Volume
33
Category
Article
ISSN
0899-8418

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

Given distinct climatic periods in the various facets of the Earth's climate system, many attempts have been made to determine the exact timing of β€˜change points’ or regime boundaries. However, identification of change points is not always a simple task. A time series containing N data points has approximately N^k^ distinct placements of k change points, rendering brute force enumeration futile as the length of the time series increases. Moreover, how certain are we that any one placement of change points is superior to the rest? This paper introduces a Bayesian Change Point algorithm which provides uncertainty estimates both in the number and location of change points through an efficient probabilistic solution to the multiple change point problem. To illustrate its versatility, the Bayesian Change Point algorithm is used to analyse both the NOAA/NCDC annual global surface temperature anomalies time series and the much longer Ξ΄^18^O record of the Plio‐Pleistocene. Copyright Β© 2012 Royal Meteorological Society


πŸ“œ SIMILAR VOLUMES


Bayesian approach to change points detec
✍ Ali Mohammad-Djafari; Olivier FΓ©ron πŸ“‚ Article πŸ“… 2006 πŸ› John Wiley and Sons 🌐 English βš– 424 KB

## Abstract Change points detection in time series is an important area of research in statistics, has a long history and has many applications. However, very often change point analysis is only focused on the changes in the mean value of some quantity in a process. In this work we consider time se

Subtle changes in benign tissue adjacent
✍ Montironi, Rodolfo; Diamanti, Lucilla; Pomante, Roberto; Thompson, Deborah; Bart πŸ“‚ Article πŸ“… 1997 πŸ› John Wiley and Sons 🌐 English βš– 136 KB

The aim of this paper was to test the usefulness of a Bayesian belief network (BBN) as a decision support system in the uncertainty assessment of benign prostatic tissue, either associated or not with inflammation or adjacent to prostatic adenocarcinoma (PAC) or prostatic intraepithelial neoplasia (