Bayesian Analysis

On the Geometry of Bayesian Inference

Miguel de Carvalho, Garritt L. Page, and Bradley J. Barney

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We provide a geometric interpretation to Bayesian inference that allows us to introduce a natural measure of the level of agreement between priors, likelihoods, and posteriors. The starting point for the construction of our geometry is the observation that the marginal likelihood can be regarded as an inner product between the prior and the likelihood. A key concept in our geometry is that of compatibility, a measure which is based on the same construction principles as Pearson correlation, but which can be used to assess how much the prior agrees with the likelihood, to gauge the sensitivity of the posterior to the prior, and to quantify the coherency of the opinions of two experts. Estimators for all the quantities involved in our geometric setup are discussed, which can be directly computed from the posterior simulation output. Some examples are used to illustrate our methods, including data related to on-the-job drug usage, midge wing length, and prostate cancer.

Article information

Bayesian Anal., Advance publication (2018), 24 pages.

First available in Project Euclid: 10 August 2018

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Bayesian inference geometry Hellinger affinity Hilbert space marginal likelihood

Creative Commons Attribution 4.0 International License.


de Carvalho, Miguel; Page, Garritt L.; Barney, Bradley J. On the Geometry of Bayesian Inference. Bayesian Anal., advance publication, 10 August 2018. doi:10.1214/18-BA1112.

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Supplemental materials

  • Supplementary Material to “On the Geometry of Bayesian Inference”. The online supplementary materials include the counterparts of the data examples in the paper for the case of affine-compatibility as introduced in Section 3.2, technical derivations, and proofs of propositions.