AI-Powered Bayesian Inference

TL;DR

This paper introduces a Bayesian framework that integrates AI-generated predictions as priors, enabling coherent uncertainty quantification and inference by combining AI outputs with observed data within a non-parametric Bayesian approach.

Contribution

It proposes a novel non-parametric Bayesian method using Dirichlet process priors with AI models as baselines, allowing out-of-sample tuning and rapid posterior simulation.

Findings

Enables AI-informed Bayesian inference with uncertainty quantification.

Allows parallelized and optimized posterior sampling.

Provides a flexible framework for integrating AI predictions into statistical analysis.

Abstract

The advent of Generative Artificial Intelligence (GAI) has heralded an inflection point that changed how society thinks about knowledge acquisition. While GAI cannot be fully trusted for decision-making, it may still provide valuable information that can be integrated into a decision pipeline. Rather than seeing the lack of certitude and inherent randomness of GAI as a problem, we view it as an opportunity. Indeed, variable answers to given prompts can be leveraged to construct a prior distribution which reflects assuredness of AI predictions. This prior distribution may be combined with tailored datasets for a fully Bayesian analysis with an AI-driven prior. In this paper, we explore such a possibility within a non-parametric Bayesian framework. The basic idea consists of assigning a Dirichlet process prior distribution on the data-generating distribution with AI generative model as its baseline. Hyper-parameters of the prior can be tuned out-of-sample to assess the informativeness of the AI prior. Posterior simulation is achieved by computing a suitably randomized functional on an augmented data that consists of observed (labeled) data as well as fake data whose labels have been imputed using AI. This strategy can be parallelized and rapidly produces iid samples from the posterior by optimization as opposed to sampling from conditionals. Our method enables (predictive) inference and uncertainty quantification leveraging AI predictions in a coherent probabilistic manner.