Neural-Inspired Posterior Approximation (NIPA)

2026-01-30

AI Summary

This work addresses the challenge of balancing efficient posterior exploration and principled uncertainty quantification in large-scale Bayesian inference. Inspired by the human brain’s multi-system neural architecture, it proposes a unified Bayesian sampling framework that integrates three cognitive mechanisms: model-based planning, model-free habitual response, and episodic memory. By synergistically combining target-distribution-guided sampling, learning from patterns in historical samples, and direct retrieval of specific past samples through episodic recall, the method constructs a hybrid sampling strategy. This approach significantly enhances the scalability and computational efficiency of posterior approximation while preserving rigorous uncertainty quantification, thereby offering a novel pathway for applying Bayesian deep learning to large-scale problems.

Technology Category

• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Probabilistic Programming
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Humans learn efficiently from their environment by engaging multiple interacting neural systems that support distinct yet complementary forms of control, including model-based (goal-directed) planning, model-free (habitual) responding, and episodic memory-based learning. Model-based mechanisms compute prospective action values using an internal model of the environment, supporting flexible but computationally costly planning; model-free mechanisms cache value estimates and build heuristics that enable fast, efficient habitual responding; and memory-based mechanisms allow rapid adaptation from individual experience. In this work, we aim to elucidate the computational principles underlying this biological efficiency and translate them into a sampling algorithm for scalable Bayesian inference through effective exploration of the posterior distribution. More specifically, our proposed algorithm comprises three components: a model-based module that uses the target distribution for guided but computationally slow sampling; a model-free module that uses previous samples to learn patterns in the parameter space, enabling fast, reflexive sampling without directly evaluating the expensive target distribution; and an episodic-control module that supports rapid sampling by recalling specific past events (i.e., samples). We show that this approach advances Bayesian methods and facilitates their application to large-scale statistical machine learning problems. In particular, we apply our proposed framework to Bayesian deep learning, with an emphasis on proper and principled uncertainty quantification.

Problem

• Bayesian inference
• posterior approximation
• scalable sampling
• uncertainty quantification
• Bayesian deep learning

Innovation

• Neural-Inspired Posterior Approximation
• Bayesian inference
• model-based planning
• model-free learning
• episodic control