Sammanfattning
The Bayesian CAR (continuous autoregressive) model for accelerator mass spectrometry (AMS) data analysis
delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine
are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte
Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the GPL license. Included
in the package is an R program that analyzes the car4ams output and summarizes the results in graphical and spreadsheet for-
mats.We describe the main properties of the CAR analysis and the use of the 2 parts of the program package for radiocarbon
AMS data analysis.
delivers uncertainties with less scatter and bias. Better detection and estimation of the instrumental error of the AMS machine
are also achieved. Presently, the main disadvantage is the several-hour duration of the analysis. The Markov chain Monte
Carlo (MCMC) program for CAR model analysis, car4ams, has been made freely available under the GPL license. Included
in the package is an R program that analyzes the car4ams output and summarizes the results in graphical and spreadsheet for-
mats.We describe the main properties of the CAR analysis and the use of the 2 parts of the program package for radiocarbon
AMS data analysis.
| Originalspråk | engelska |
|---|---|
| Tidskrift | Radiocarbon |
| Volym | 2010 |
| Nummer | 52(3) |
| Sidor (från-till) | 948-952 |
| Antal sidor | 5 |
| ISSN | 0033-8222 |
| Status | Publicerad - 2010 |
| MoE-publikationstyp | A1 Tidskriftsartikel-refererad |
Vetenskapsgrenar
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