Neural-Inspired Posterior Approximation (NIPA)

• URL: http://arxiv.org/abs/2601.22539v1
• Date: Fri, 30 Jan 2026 04:19:26 GMT
• Title: Neural-Inspired Posterior Approximation (NIPA)
• Authors: Babak Shahbaba, Zahra Moslemi,
• Abstract summary: Humans learn efficiently from their environment by engaging multiple interacting neural systems.<n>We aim to elucidate the computational principles underlying this biological efficiency and translate them into a sampling algorithm.<n>We show that this approach advances Bayesian methods and facilitates their application to large-scale statistical machine learning problems.
• Score: 1.1649834448993244
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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