The posterior over model parameters in Bayesian Neural Networks is expressed using Bayes’ theorem as p(θ | X, Y) = [p(Y | X, θ) * p(θ)] / p(Y | X), where θ represents the parameters of the neural network, X represents input features, Y represents target outputs, p(Y | X, θ) is the likelihood, p(θ) is the prior distribution, and p(Y | X) is the marginal likelihood.

Authors

• Person: Not available Organization: Springer
  A Comprehensive Review of Neuro-symbolic AI for Robustness ...

Sources

• A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer via serper

Referenced by nodes (1)

• Bayesian Neural Networks concept