AI-Powered Bayesian Inference

• URL: http://arxiv.org/abs/2502.19231v2
• Date: Tue, 01 Apr 2025 15:27:51 GMT
• Title: AI-Powered Bayesian Inference
• Authors: Veronika Ročková, Sean O'Hagan,
• Abstract summary: Generative Artificial Intelligence (GAI) has heralded an inflection point that changed how society thinks about knowledge acquisition.<n>While GAI cannot be fully trusted for decision-making, it may still provide valuable information that can be integrated into a decision pipeline.<n> variable answers to given prompts can be leveraged to construct a prior distribution which reflects assuredness of AI predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Conformal Validity Guarantees Exist for Any Data Distribution (and How to Find Them) [14.396431159723297]
  We show that conformal prediction can theoretically be extended to textitany joint data distribution. Although the most general case is exceedingly impractical to compute, for concrete practical applications we outline a procedure for deriving specific conformal algorithms.
  arXiv  Detail & Related papers  (2024-05-10T17:40:24Z)
• Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability [50.44439018155837]
  We propose to include a calibration term directly into the training objective of the neural model. By introducing a relaxation of the classical formulation of calibration error we enable end-to-end backpropagation. It is directly applicable to existing computational pipelines allowing reliable black-box posterior inference.
  arXiv  Detail & Related papers  (2023-10-20T10:20:45Z)
• Calibrated Explanations for Regression [1.2058600649065616]
  Calibrated Explanations for regression provides fast, reliable, stable, and robust explanations. Calibrated Explanations for probabilistic regression provides an entirely new way of creating explanations. An implementation in Python is freely available on GitHub and for installation using both pip and conda.
  arXiv  Detail & Related papers  (2023-08-30T18:06:57Z)
• Calibrating AI Models for Wireless Communications via Conformal Prediction [55.47458839587949]
  Conformal prediction is applied for the first time to the design of AI for communication systems. This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees.
  arXiv  Detail & Related papers  (2022-12-15T12:52:23Z)
• Principled Knowledge Extrapolation with GANs [92.62635018136476]
  We study counterfactual synthesis from a new perspective of knowledge extrapolation. We show that an adversarial game with a closed-form discriminator can be used to address the knowledge extrapolation problem. Our method enjoys both elegant theoretical guarantees and superior performance in many scenarios.
  arXiv  Detail & Related papers  (2022-05-21T08:39:42Z)
• Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
  Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
  arXiv  Detail & Related papers  (2021-09-01T09:52:31Z)
• Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits [18.740781076082044]
  We propose an approach to overcome the independence assumption behind most of the approaches dealing with a large class of probabilistic reasoning. We provide an algorithm for Bayesian learning from sparse, albeit complete, observations. Each leaf of such circuits is labelled with a beta-distributed random variable that provides us with an elegant framework for representing uncertain probabilities.
  arXiv  Detail & Related papers  (2021-02-22T10:03:15Z)
• Improving Uncertainty Calibration via Prior Augmented Data [56.88185136509654]
  Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. They are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions. We propose a solution by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels.
  arXiv  Detail & Related papers  (2021-02-22T07:02:37Z)
• General stochastic separation theorems with optimal bounds [68.8204255655161]
  Phenomenon of separability was revealed and used in machine learning to correct errors of Artificial Intelligence (AI) systems and analyze AI instabilities. Errors or clusters of errors can be separated from the rest of the data. The ability to correct an AI system also opens up the possibility of an attack on it, and the high dimensionality induces vulnerabilities caused by the same separability.
  arXiv  Detail & Related papers  (2020-10-11T13:12:41Z)
• PrognoseNet: A Generative Probabilistic Framework for Multimodal Position Prediction given Context Information [2.5302126831371226]
  We propose an approach which reformulates the prediction problem as a classification task, allowing for powerful tools. A smart choice of the latent variable allows for the reformulation of the log-likelihood function as a combination of a classification problem and a much simplified regression problem. The proposed approach can easily incorporate context information and does not require any preprocessing of the data.
  arXiv  Detail & Related papers  (2020-10-02T06:13:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.