ELBO

E835245

ELBO (Evidence Lower Bound) is an objective function used in variational inference to approximate complex probability distributions, particularly in variational autoencoders and related Bayesian models.

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Statements (49)

Predicate Object
instanceOf objective function
variational inference concept
alternativeForm log p(x) - KL(q(z|x) || p(z|x))
bounds log marginal likelihood from below
canBe decomposed into sum over data points
estimated with Monte Carlo samples
centralRoleIn training of variational autoencoders (VAEs)
definedOver latent variables
observed variables
variational distribution
dependsOn model parameters
variational parameters
fullName Evidence Lower Bound
hasTerm reconstruction term
regularization term
introducedInContextOf variational methods in Bayesian statistics
isLowerBoundOn log p(x)
mathematicalDomain machine learning
probability theory
maximizationEquivalentTo minimizing KL divergence between variational posterior and true posterior
optimizedBy reparameterization trick
stochastic gradient descent
stochastic variational inference
property non-convex in general for deep models
tighter ELBO implies better approximation to true posterior
regularizationTermOften KL(q(z|x) || p(z))
relatedTo Kullback–Leibler divergence NERFINISHED
log evidence
marginal likelihood
negative variational free energy
variational free energy
typicalForm E_q[log p(x,z)] - E_q[log q(z)]
usedFor approximating complex probability distributions
learning generative models
optimizing variational distributions
training probabilistic models with latent variables
usedIn Bayesian inference
Bayesian neural networks NERFINISHED
amortized variational inference
approximate Bayesian inference
black-box variational inference
deep generative models
latent variable models
probabilistic programming
variational autoencoder
variational inference
variant beta-ELBO
hierarchical ELBO
importance-weighted ELBO

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