Abstract

The broad goal of this presentation is to demonstrate the utility of probabilistic generative models to capture investigators' knowledge of geological processes and proxy data to draw statistical inferences about unobserved paleoclimatological events. We illustrate how this approach forces investigators to be explicit about their assumptions, and about how probability theory yields results that are a mathematical consequence of these assumptions and the data. We illustrate these ideas with the HMM-Match model that infers common times of sediment deposition in two records and the uncertainty in these inferences in the form of confidence bands. HMM-Match models the sedimentation processes that led to proxy data measured in marine sediment cores. This Bayesian model has three components: 1) a generative probabilistic model that proceeds from the underlying geophysical and geochemical events, specifically …

Date

2014

Authors

C Lawrence, L Lin, LE Lisiecki, D Khider

Journal

AGU Fall Meeting Abstracts

Volume

2014

Pages

PP41D-1427