Module: ed.inferences
Defined in edward/inferences/__init__.py.
Classes
class BiGANInference: Adversarially Learned Inference (Dumoulin et al., 2017) or
class GANInference: Parameter estimation with GAN-style training
class Gibbs: Gibbs sampling (Geman & Geman, 1984).
class HMC: Hamiltonian Monte Carlo, also known as hybrid Monte Carlo
class ImplicitKLqp: Variational inference with implicit probabilistic models
class Inference: Abstract base class for inference. All inference algorithms in
class KLpq: Variational inference with the KL divergence
class KLqp: Variational inference with the KL divergence
class Laplace: Laplace approximation (Laplace, 1986).
class MAP: Maximum a posteriori.
class MetropolisHastings: Metropolis-Hastings (Hastings, 1970; Metropolis, Rosenbluth, Rosenbluth, Teller, & Teller, 1953).
class MonteCarlo: Abstract base class for Monte Carlo. Specific Monte Carlo methods
class ReparameterizationEntropyKLqp: Variational inference with the KL divergence
class ReparameterizationKLKLqp: Variational inference with the KL divergence
class ReparameterizationKLqp: Variational inference with the KL divergence
class ReplicaExchangeMC: Replica Exchange MCMC (Hukushima & Nemoto, 1996; Swendsen & Wang, 1986).
class SGHMC: Stochastic gradient Hamiltonian Monte Carlo (Chen, Fox, & Guestrin, 2014).
class SGLD: Stochastic gradient Langevin dynamics (Welling & Teh, 2011).
class ScoreEntropyKLqp: Variational inference with the KL divergence
class ScoreKLKLqp: Variational inference with the KL divergence
class ScoreKLqp: Variational inference with the KL divergence
class ScoreRBKLqp: Variational inference with the KL divergence
class VariationalInference: Abstract base class for variational inference. Specific
class WGANInference: Parameter estimation with GAN-style training
class WakeSleep: Wake-Sleep algorithm (Hinton, Dayan, Frey, & Neal, 1995).
Functions
complete_conditional(...): Returns the conditional distribution RandomVariable
Chen, T., Fox, E. B., & Guestrin, C. (2014). Stochastic gradient Hamiltonian Monte Carlo. In International conference on machine learning.
Dumoulin, V., Belghazi, I., Poole, B., Lamb, A., Arjovsky, M., Mastropietro, O., & Courville, A. (2017). Adversarially Learned Inference. In International conference on learning representations.
Geman, S., & Geman, D. (1984). Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6), 721–741.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109.
Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995). The "wake-sleep" algorithm for unsupervised neural networks. Science.
Hukushima, K., & Nemoto, K. (1996). Exchange monte carlo method and application to spin glass simulations. Journal of the Physical Society of Japan, 65(6), 1604–1608.
Laplace, P. S. (1986). Memoir on the probability of the causes of events. Statistical Science, 1(3), 364–378.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092.
Swendsen, R. H., & Wang, J.-S. (1986). Replica monte carlo simulation of spin-glasses. Physical Review Letters, 57(21), 2607.
Welling, M., & Teh, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. In International conference on machine learning.