Posterior and variational inference for deep neural networks with heavy-tailed weights.
Ismaël CastilloPaul EgelsPublished in: CoRR (2024)
Keyphrases
- variational inference
- heavy tailed
- posterior distribution
- gaussian process
- probabilistic model
- probability distribution
- bayesian inference
- latent variables
- bayesian framework
- parameter estimation
- hyperparameters
- markov chain monte carlo
- topic models
- posterior probability
- maximum a posteriori
- mixture model
- regression model
- approximate inference
- closed form
- gaussian distribution
- expectation maximization
- support vector
- bayesian networks
- cross validation
- model selection
- prior knowledge
- density estimation
- maximum likelihood
- graphical models