Philosophy and the experience of Bayesian data analysis

Philosophy and the practice of Bayesian statistics in the social sciences

I present my own perspective on the philosophy of Bayesian statistics, based on my experiences doing applied statistics in the social sciences and elsewhere. My motivation for this project is dissatisfaction with what I perceive as the standard view of the philosophical foundations of Bayesian statistics, a view in which Bayesian inference is inductive and scientific learning proceeds via the computation of the posterior probability of hypotheses. In contrast, I view Bayesian inference as deductive and as part of a larger Bayesian data-analytic process, different parts of which I believe can be usefully understood in light of the philosophical frameworks of Popper, Kuhn, and Lakatos. The practical implication of my philosophy is to push Bayesian data analysis toward a continual creative-destruction process of model building, inference, and model-checking rather than to aim for an overarching framework of scientific learning via posterior probabilities of hypotheses. This work is joint with Cosma Shalizi.

Paris Diderot Philmath Seminar
Lundi 15 février 2010 à 14h-15h30

Université Paris Diderot – Site Rive Gauche, Bâtiment Condorcet
4 rue Elsa Morante
75205 PARIS CEDEX 13
Salle Klee (454A)

10 thoughts on “Philosophy and the experience of Bayesian data analysis

  1. I am intrigued to read the full text of this argument/thesis. Look forward to more details on this from you. Also, is it possible to watch the video of the talk on the web?

  2. That's OK, Andrew, you can't make me read them.

    If you take Gerardo's advice, then I will retract my statement that I don't want to see the talk. In fact, in that case I would love to see the talk.

  3. Phil: I'm not quite sure how to speak French with an outrageous French accent. No matter how hard I try, I think it still comes out with an outrageous American accent. But, for you, I'll try.

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