On the Variational Posterior of Dirichlet Process Deep Latent Gaussian Mixture Models.
Amine EchraibiJoachim Flocon-CholetStéphane GosselinSandrine VatonPublished in: CoRR (2020)
Keyphrases
- gaussian mixture model
- mixture model
- dirichlet process
- dirichlet processes
- posterior distribution
- latent variables
- probabilistic model
- dirichlet process mixture models
- free energy
- expectation maximization
- em algorithm
- markov chain monte carlo
- variational methods
- gaussian distribution
- generative model
- maximum likelihood
- bayesian framework
- density estimation
- latent topics
- image segmentation
- variational bayes
- parameter estimation
- bayesian model
- variational inference
- probability distribution
- model selection
- posterior probability
- probability density function
- language model
- hyperparameters
- maximum a posteriori
- prior knowledge
- gaussian processes
- unsupervised learning
- dirichlet distribution
- feature vectors
- graphical models
- gaussian process
- multi task learning
- training data
- bayesian inference
- image processing
- topic models
- exponential family
- sampling algorithm
- approximate inference
- machine learning