Gallagher, K.; Bodin, Thomas; Sambridge, Malcolm; Weiss, Dominik; Kylander, Malin; Large, D J
We present a method to quantify abrupt changes (or changepoints) in data series, represented as a function of depth or time. These changes are often the result of climatic or environmental variations and can be manifested in multiple datasets as different responses, but all datasets can have the same changepoint locations/timings. The method we present uses transdimensional Markov chain Monte Carlo to infer probability distributions on the number and locations (in depth or time) of...[Show more]
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