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Has there been a structural change in the way U.S. presidents use force abroad since the nineteenth century? In this article, I investigate historical changes in the use of force by U.S. presidents using Bayesian changepoint analysis. In doing so, I present an integrated Bayesian approach for analyzing changepoint problems in a Poisson regression model. To find the nature of the breaks, I estimate parameters of the Poisson regression changepoint model using Chib's (1998) hidden Markov model algorithm and Frühwirth-Schnatter and Wagner's (2006) data augmentation method. Then, I utilize transdimensional Markov chain Monte Carlo methods to detect the number of breaks. Analyzing yearly use of force data from 1890 to 1995, I find that, controlling for the effects of the Great Depression and the two world wars, the relationship between domestic conditions and the frequency of the use of force abroad fundamentally shifted in the 1940s.