Markov Chain Monte Carlo inference on graphical models using event-based processing on the SpiNNaker neuromorphic architecture.
Daniel R. MendatSang Peter ChinSteve B. FurberAndreas G. AndreouPublished in: CISS (2015)
Keyphrases
- markov chain monte carlo
- graphical models
- approximate inference
- exact inference
- loopy belief propagation
- belief propagation
- probabilistic inference
- probabilistic model
- nonparametric belief propagation
- factor graphs
- variational methods
- importance sampling
- dynamic bayesian networks
- bayesian networks
- bayesian inference
- conditional random fields
- random variables
- probabilistic graphical models
- parameter estimation
- generative model
- map inference
- structure learning
- markov logic
- message passing
- exponential family
- markov networks
- parameter learning
- structured prediction
- partition function
- monte carlo
- posterior probability
- posterior distribution
- belief networks
- markov chain
- gaussian process
- statistical relational learning
- particle filter
- directed graphical models
- machine learning
- latent variables
- image reconstruction
- maximum likelihood
- simulated annealing
- hidden markov models