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arXiv · 1512.07678

Composite Bayesian inference

Alexis Roche
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Abstract

We revisit and generalize the concept of composite likelihood as a method to make a probabilistic inference by aggregation of multiple Bayesian agents, thereby defining a class of predictive models which we call composite Bayesian. This perspective gives insight to choose the weights associated with composite likelihood, either a priori or via learning; in the latter case, they may be tuned so as to minimize prediction cross-entropy, yielding an easy-to-solve convex problem. We argue that composite Bayesian inference is a middle way between generative and discriminative models that trades off between interpretability and prediction performance, both of which are crucial to many artificial intelligence tasks.

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Alexis Roche. 2019-04-17. Composite Bayesian inference. https://arxiv.org/abs/1512.07678

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