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Neural network treating weights as probability distributions and inferring posteriors to capture predictive uncertainty.
DESCRIPTION
A Bayesian neural network (BNN) places prior distributions over weights and infers posterior distributions given data. This yields principled uncertainty estimates and can improve calibration and robustness. In practice, exact inference is intractable; approximations include variational inference (e.g., Bayes by Backprop) and Monte Carlo methods. BNNs are valuable in risk-sensitive domains and active learning. Deployment considerations include sampling overhead and specialized libraries like TensorFlow Probability or Pyro.
ALTERNATIVE DEFINITIONS
Wikipedia
A Bayesian neural network is a neural network with a prior distribution on its weights; Bayesian inference is then used to infer an approximate posterior distribution over the network’s weights.
Highlights priors over weights and posterior inference.
Blundell et al. (2015)
We introduce Bayes by Backprop, a method for variational inference in neural networks. Our approach learns a distribution over the weights of a neural network.
Presents a practical variational approach for BNNs.
TensorFlow Probability
In a Bayesian neural network, we place a prior distribution over weights and use variational inference to approximate the posterior given data.
Framework-centric description focusing on variational layers.
RELATED TERMS
Bayesian Inference Variational Inference Bayes by Backprop Uncertainty Estimation Calibration Monte Carlo Dropout Gaussian Processes Prior
COMPETING TERMS
Deterministic Neural Network Deep Ensembles
RELATIONS
- Broader: Neural Network
KNOWLEDGE AREAS
TARGET AUDIENCE
AI Engineer Data Scientist MLOps Engineer
EXTERNAL SOURCES
- Wikipedia - Bayesian neural network
- arXiv - Bayes by Backprop paper
- TensorFlow Probability - Probabilistic modeling library
- Pyro - Bayesian regression example
TOP QUESTIONS
Why use a Bayesian Neural Network?
BNNs provide principled uncertainty estimates, improving decision-making in safety-critical or data-sparse settings.
Category: Relevancy
How do BNNs differ from deterministic networks?
BNNs infer distributions over weights and outputs; deterministic networks learn fixed weights.
Category: Comparison
What inference methods are used for BNNs?
Variational inference, Markov chain Monte Carlo, and approximate methods like Monte Carlo dropout.
Category: Technique
Are BNNs computationally expensive?
Yes, sampling and additional parameters increase training and inference cost versus deterministic models.
Category: Tradeoff
How do BNNs improve calibration?
By modeling uncertainty, predictions align better with true outcome frequencies across confidence levels.
Category: Benefit
When should I prefer deep ensembles over BNNs?
Ensembles can be easier to implement and scale, offering strong uncertainty estimates at extra compute cost.
Category: Decision
What priors are common in BNNs?
Gaussian priors are typical; heavier-tailed priors can improve robustness and sparsity.
Category: Concept
How do BNNs support active learning?
Uncertainty estimates guide acquisition functions to query the most informative samples for labeling.
Category: Application
What libraries support BNNs?
TensorFlow Probability, Pyro, NumPyro, and Edward2 offer probabilistic layers and inference tools.
Category: Tools