Variable Selection with Rigorous Uncertainty Quantification using Deep Bayesian Neural Networks: Posterior Concentration and Bernstein-von Mises Phenomenon.
Jeremiah LiuPublished in: AISTATS (2021)
Keyphrases
- variable selection
- von mises
- neural network
- posterior distribution
- posterior probability
- cross validation
- hyperparameters
- model selection
- input variables
- gaussian distribution
- probability density function
- probability distribution
- high dimensional
- pattern recognition
- bayesian networks
- conditional probabilities
- bayesian framework
- artificial neural networks
- dimension reduction
- prior distribution
- gaussian process
- fuzzy logic
- neural network model
- bayesian inference
- image processing
- parameter estimation
- high dimensional data
- mixture model
- maximum likelihood
- probabilistic model
- pairwise
- gaussian processes
- heavy tailed
- data sets
- feature selection
- maximum a posteriori
- generative model
- low dimensional
- training set
- feature space
- support vector
- feature extraction