Add to ORKGStochastic variational inference makes it possible to approximate posterior distributions induced by large datasets quickly. The algorithm relies heav-ily on the use of fully factorized variational dis-tributions. However, this “mean-field ” indepen-dence approximation introduces bias. We show how to relax the mean-field approximation to al-low arbitrary dependences between global pa-rameters and local hidden variables, reducing both bias and sensitivity to local optima.
The ill-posed nature of missing variable models offers a challenging testing ground for new computat...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
Variational inference has become a widely used method to approximate posteriors in complex latent va...
Abstract Stochastic variational inference makes it possible to approximate posterior distributions i...
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approx...
The field of statistical machine learning has seen a rapid progress in complex hierarchical Bayesian...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
<p>Stochastic variational inference finds good posterior approximations of probabilistic models with...
Stochastic variational inference is a promising method for fitting large-scale probabilistic models ...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for l...
Stochastic variational inference finds good posterior approximations of probabilistic models with ve...
Variational inference is a popular alternative to Markov chain Monte Carlo methods that constructs ...
We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic g...
Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation...
The ill-posed nature of missing variable models offers a challenging testing ground for new computat...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
Variational inference has become a widely used method to approximate posteriors in complex latent va...
Abstract Stochastic variational inference makes it possible to approximate posterior distributions i...
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approx...
The field of statistical machine learning has seen a rapid progress in complex hierarchical Bayesian...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
<p>Stochastic variational inference finds good posterior approximations of probabilistic models with...
Stochastic variational inference is a promising method for fitting large-scale probabilistic models ...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for l...
Stochastic variational inference finds good posterior approximations of probabilistic models with ve...
Variational inference is a popular alternative to Markov chain Monte Carlo methods that constructs ...
We investigate a local reparameterizaton technique for greatly reducing the variance of stochastic g...
Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation...
The ill-posed nature of missing variable models offers a challenging testing ground for new computat...
Variational approximation methods are enjoying an increasing amount of development and use in statis...
Variational inference has become a widely used method to approximate posteriors in complex latent va...