Variational inference and sparsity in high-dimensional deep Gaussian mixture models.
Lucas KockNadja KleinDavid J. NottPublished in: Stat. Comput. (2022)
Keyphrases
- variational inference
- high dimensional
- gaussian mixture model
- mixture model
- probabilistic model
- density estimation
- em algorithm
- low dimensional
- generative model
- feature space
- expectation maximization
- dimensionality reduction
- bayesian inference
- exponential family
- model selection
- topic models
- gaussian process
- unsupervised learning
- posterior distribution
- data points
- high dimensional data
- dirichlet process
- latent dirichlet allocation
- maximum likelihood
- variational methods
- language model
- variational bayes
- probability density function
- parameter space
- closed form
- gaussian distribution
- hyperparameters
- image segmentation
- information retrieval
- parameter estimation
- graphical models
- feature vectors
- bayesian networks
- clustering algorithm
- feature selection
- data mining