The Structure and Dynamics of Scientific Theories: A Hierarchical Bayesian Perspective

Philosophy of Science 77 (2):172-200 (2010)

Authors
James Woodward
University of Pittsburgh
Leah Henderson
University of Groningen
Abstract
Hierarchical Bayesian models (HBMs) provide an account of Bayesian inference in a hierarchically structured hypothesis space. Scientific theories are plausibly regarded as organized into hierarchies in many cases, with higher levels sometimes called ‘paradigms’ and lower levels encoding more specific or concrete hypotheses. Therefore, HBMs provide a useful model for scientific theory change, showing how higher‐level theory change may be driven by the impact of evidence on lower levels. HBMs capture features described in the Kuhnian tradition, particularly the idea that higher‐level theories guide learning at lower levels. In addition, they help resolve certain issues for Bayesians, such as scientific preference for simplicity and the problem of new theories. *Received July 2009; revised October 2009. †To contact the authors, please write to: Leah Henderson, Massachusetts Institute of Technology, 77 Massachusetts Avenue, 32D‐808, Cambridge, MA 02139; e‐mail: lhenders@mit.edu.
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DOI 10.1086/651319
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Abduction.Igor Douven - 2011 - Stanford Encyclopedia of Philosophy.
The Demarcation Problem: A (Belated) Response to Laudan.Massimo Pigliucci - 2013 - In Massimo Pigliucci & Maarten Boudry (eds.), Philosophy of Pseudoscience: Reconsidering the Demarcation Problem. University of Chicago Press. pp. 9.
Inference to the Best Explanation Made Incoherent.Nevin Climenhaga - 2017 - Journal of Philosophy 114 (5):251-273.
Bayesianism and Inference to the Best Explanation.Leah Henderson - 2013 - British Journal for the Philosophy of Science 65 (4):687-715.

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