Browsing Radiocarbon, Volume 43, Number 2A (2001 by Subjects
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New Methods and Critical Aspects in Bayesian Mathematics for 14C CalibrationThe probabilistic radiocarbon calibration approach, which largely has replaced the intercept method in 14C dating, is based on the so-called Bayes' theorem (Bayes 1763). Besides single-sample calibration, Bayesian mathematics also supplies tools for combining 14C results of many samples with independent archaeological information such as typology or stratigraphy (Buck et al. 1996). However, specific assumptions in the "prior probabilities", used to transform the archaeological information into mathematical probability distributions, may bias the results (Steier and Rom 2000). A general technique for guarding against such a bias is "sensitivity analysis", in which a range of possible prior probabilities is tested. Only results that prove robust in this analysis should be used. We demonstrate the impact of this method for an assumed, yet realistic case of stratigraphically ordered samples from the Hallstatt period, i.e. The Early Iron Age in Central Europe.
Solar Activity and Regional ClimateWe performed a statistical analysis of the data on summer temperature anomalies in northern Fennoscandia (8-1995 AD) and found that a 70-130-yr cycle is present in this series during most of the time period. A comparison of the reconstructed northern Fennoscandia temperature with different indicators of solar activity (Wolf numbers, the length of solar Schwabe cycle, extended bi-decadal radiocarbon series, and data on sunspots observed by naked eye) shows that the more probable cause of the periodicity is the modulation of regional northern Fennoscandia climate by the long-term solar cycle of Gleissberg. The effect of this century-scale solar modulation of the global Northern Hemisphere temperature is weaker.
The Filling of Gaps in Geophysical Time Series by Artificial Neural NetworksNowadays, there is a large number of time series of natural data to study geophysical and astrophysical phenomena and their characteristics. However, short length and data gaps pose a substantial problem for obtaining results on properties of the underlying physical phenomena with existing algorithms. Using only an equidistant subset of the data with coarse steps leads to loss of information. We present a method to recover missing data in time series. The approach is based on modeling the time series with manifolds of small dimension, and it is implemented with the help of neural networks. We applied this approach to real data on cosmogenic isotopes, demonstrating that it could successfully repair gaps where data was purposely left out. Multi-fractal analysis was applied to a true radiocarbon time series after recovering missing data.