Bayesian Neural Network

Definitions

1. A neural network that models uncertainty by treating weights or predictions probabilistically rather than deterministically. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.

   In plain English: Bayesian Neural Network is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.

   Example: "We revisited Bayesian Neural Network during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."

Related Terms

• Activation Function
• Autoencoder
• Backpropagation
• Bayesian Optimization
• Boltzmann Machine
• Conformal Prediction
• GAN
• Gated Recurrent Unit
• Gaussian Process
• Graph Convolutional Network
• Graph Neural Network
• Sigmoid Function