Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Squared families are useful conjugate priors
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Investigates squared families of distributions as conjugate prior beliefs for Bayesian inference, rather than as likelihoods.
Problem
Squared families are typically used as likelihoods in machine learning; their use as prior distributions in Bayesian inference is not well explored.
Approach
Uses squared families (distributions proportional to the square of a function from a family) as prior beliefs, exploiting their computational, geometric and statistical properties in the prior role, where they act as conjugate priors.
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