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Bayesian Statistical Inference and Approximate Truth

Bayesian statisticians often study models and hypotheses that they know to be false. This creates an interpretive problem because the Bayesian probability of a hypothesis or model is supposed to represent the probability that the hypothesis or model is true. I investigate whether Bayesianism can accommodate the idea that false models and hypotheses are sometimes approximately true or that some hypotheses or models can be closer to the truth than others. I argue that approximate truth is hard to square with Bayesianism, but that closeness to the truth can be made compatible with Bayesianism, and that this provides an adequate and potentially useful solution to the interpretive problem, although the solution has costs.Author Information:

Olav Benjamin Vassend

Philosophy

University of Wisconsin--Madison