Add to ORKGStochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation to massive data. We present an alternative perspective on SVI as approximate parallel coordinate ascent. SVI trades-off bias and variance to step close to the unknown true coordinate optimum given by batch variational Bayes (VB). We define a model to automate this process. The model infers the lo-cation of the next VB optimum from a sequence of noisy realizations. As a conse-quence of this construction, we update the variational parameters using Bayes rule, rather than a hand-crafted optimization schedule. When our model is a Kalman filter this procedure can recover the original SVI algorithm and SVI with adaptive steps. We may also encode a...
Variational inference is a popular alternative to Markov chain Monte Carlo methods that constructs ...
Model-checking for parametric stochastic models can be expressed as checking the satisfaction probab...
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse...
Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation...
The field of statistical machine learning has seen a rapid progress in complex hierarchical Bayesian...
Variational inference algorithms have proven successful for Bayesian analysis in large data settings...
Stochastic variational inference finds good posterior approximations of probabilistic models with ve...
Stochastic variational inference finds good posterior approximations of probabilistic models with ve...
Stochastic variational inference is a promising method for fitting large-scale probabilistic models ...
Bayesian models provide powerful tools for an-alyzing complex time series data, but perform-ing infe...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Abstract Stochastic variational inference makes it possible to approximate posterior distributions i...
Many recent advances in large scale probabilistic inference rely on variational methods. The success...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approx...
Variational inference is a popular alternative to Markov chain Monte Carlo methods that constructs ...
Model-checking for parametric stochastic models can be expressed as checking the satisfaction probab...
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse...
Stochastic variational inference (SVI) uses stochastic optimization to scale up Bayesian computation...
The field of statistical machine learning has seen a rapid progress in complex hierarchical Bayesian...
Variational inference algorithms have proven successful for Bayesian analysis in large data settings...
Stochastic variational inference finds good posterior approximations of probabilistic models with ve...
Stochastic variational inference finds good posterior approximations of probabilistic models with ve...
Stochastic variational inference is a promising method for fitting large-scale probabilistic models ...
Bayesian models provide powerful tools for an-alyzing complex time series data, but perform-ing infe...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Abstract Stochastic variational inference makes it possible to approximate posterior distributions i...
Many recent advances in large scale probabilistic inference rely on variational methods. The success...
How can we perform efficient inference and learning in directed probabilistic models, in the presenc...
Mean-field variational inference is a method for approximate Bayesian posterior inference. It approx...
Variational inference is a popular alternative to Markov chain Monte Carlo methods that constructs ...
Model-checking for parametric stochastic models can be expressed as checking the satisfaction probab...
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse...