Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks.
Darío Ramos-LópezAndrés R. MasegosaAntonio SalmerónRafael RumíHelge LangsethThomas D. NielsenAnders L. MadsenPublished in: Int. J. Approx. Reason. (2018)
Keyphrases
- importance sampling
- gaussian mixture
- bayesian networks
- monte carlo
- approximate inference
- posterior distribution
- posterior probability
- expectation maximization
- markov chain
- em algorithm
- markov chain monte carlo
- kalman filter
- closed form
- gaussian mixture model
- probability distribution
- probability density function
- particle filter
- covariance matrix
- parameter estimation
- mixture model
- graphical models
- density estimation
- particle filtering
- conditional probabilities
- bayesian inference
- probabilistic model
- visual tracking
- appearance model
- incomplete data
- object tracking