Recursive Gaussian Processes and the Bayesian Brain

arXiv:2608.00503 · q-bio.NC, cs.LG, stat.ML · Submitted 2026-08-01 · Read on arXiv

Moumita Das, Dipanjan Ray, Sourabh Bhattacharya

q-bio.NC, cs.LG, stat.ML

Submitted: 2026-08-01

Comments: What is your thought process? The Bayesian Recursive Gaussian process?

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Predictive Coding — 88× in this paper · explained in 3 episodes
• Bayesian Inference — 23× in this paper · explained in 6 episodes
• Composition — 14× in this paper · explained in 2 episodes
• Uncertainty Quantification — 11× in this paper · explained in 9 episodes
• Degeneracy — 9× in this paper · explained in 4 episodes
• Hierarchical Structure — 7× in this paper · explained in 3 episodes
• Uncertainty Propagation — 6× in this paper · explained in 4 episodes
• Adam — 5× in this paper · explained in 3 episodes
• Expressivity — 5× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Hyperparameters — 3× in this paper · explained in 2 episodes
• Recursive Gaussian Process — 3× in this paper
• Robustness — 3× in this paper · explained in 6 episodes
• Coherence — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 3 episodes
• Iterative Refinement — 2× in this paper · explained in 5 episodes
• Markov Chain Monte Carlo — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Bayesian Neural Networks (BNNs) — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Fixed Point — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Langevin Dynamics — 1× in this paper · explained in 3 episodes
• Monte Carlo Dropout — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Overfitting — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 4 episodes

Sources

• Testing quasi-Dirac leptogenesis through neutrino oscillations
• Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation?

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