A Neural Network MCMC sampler that maximizes Proposal Entropy.
Zengyi LiYubei ChenFriedrich T. SommerPublished in: CoRR (2020)
Keyphrases
- markov chain monte carlo
- neural network
- markov chain
- monte carlo
- posterior distribution
- parameter estimation
- reversible jump markov chain monte carlo
- generative model
- artificial neural networks
- bayesian inference
- pattern recognition
- back propagation
- information theory
- metropolis hastings
- sampling algorithm
- importance sampling
- posterior probability
- gibbs sampling
- information theoretic
- neural network model
- particle filter
- genetic algorithm
- mutual information
- fault diagnosis
- sequential monte carlo
- latent variables
- prediction model
- neural nets
- approximate inference
- probability distribution
- simulated annealing
- fuzzy logic
- state space
- bp neural network
- probabilistic model
- prior knowledge
- bayesian networks
- gibbs sampler
- recurrent neural networks
- neural network is trained
- learning algorithm
- multilayer perceptron