A Bayesian Approach to Statistical Inference about Climate Change

Andrew R. Solow Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

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Abstract

A Bayesian approach to statistical inference about climate change based on the two-phase regression model is presented. This approach is useful when nonobservational information is available about possible climate change. This information may refer to the timing or the nature of the possible change. The approach is applied to a historic temperature record.

Abstract

A Bayesian approach to statistical inference about climate change based on the two-phase regression model is presented. This approach is useful when nonobservational information is available about possible climate change. This information may refer to the timing or the nature of the possible change. The approach is applied to a historic temperature record.

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