Stochastic variational inference is a scalable algorithm for approximating posterior distributions in probabilistic models. It is demonstrated on two probabilistic topic models: latent Dirichlet allocation and the hierarchical Dirichlet process topic model. The method is applied to large document collections, including 300K articles from Nature, 1.8M from The New York Times, and 3.8M from Wikipedia. Stochastic variational inference outperforms traditional variational inference, which can only handle smaller datasets. It enables the application of complex Bayesian models to massive data sets. The paper introduces stochastic variational inference, which uses stochastic optimization to iteratively subsample data and update variational parameters. This approach is more efficient than traditional variational inference, which requires full data passes. The method is derived for a large class of graphical models and applied to probabilistic topic models. It is shown to be effective for Bayesian nonparametric models, where the number of topics grows with the data. The algorithm is based on mean-field variational inference, which approximates the posterior distribution using a tractable distribution. The ELBO (Evidence Lower Bound) is maximized to approximate the posterior. The natural gradient is used to improve the optimization, leading to more efficient updates. Stochastic optimization is applied to the natural gradient of the ELBO, allowing the algorithm to handle large datasets by subsampling data. The paper reviews the theoretical foundations of variational inference, including the mean-field family and the natural gradient. It derives the stochastic variational inference algorithm, which iteratively updates variational parameters using subsampled data. The algorithm is shown to be efficient and scalable, enabling the analysis of massive data sets with complex probabilistic models. The method is applied to topic models, where it successfully approximates posterior topics from large document collections. The results demonstrate the effectiveness of stochastic variational inference in handling large-scale data and complex Bayesian models.Stochastic variational inference is a scalable algorithm for approximating posterior distributions in probabilistic models. It is demonstrated on two probabilistic topic models: latent Dirichlet allocation and the hierarchical Dirichlet process topic model. The method is applied to large document collections, including 300K articles from Nature, 1.8M from The New York Times, and 3.8M from Wikipedia. Stochastic variational inference outperforms traditional variational inference, which can only handle smaller datasets. It enables the application of complex Bayesian models to massive data sets. The paper introduces stochastic variational inference, which uses stochastic optimization to iteratively subsample data and update variational parameters. This approach is more efficient than traditional variational inference, which requires full data passes. The method is derived for a large class of graphical models and applied to probabilistic topic models. It is shown to be effective for Bayesian nonparametric models, where the number of topics grows with the data. The algorithm is based on mean-field variational inference, which approximates the posterior distribution using a tractable distribution. The ELBO (Evidence Lower Bound) is maximized to approximate the posterior. The natural gradient is used to improve the optimization, leading to more efficient updates. Stochastic optimization is applied to the natural gradient of the ELBO, allowing the algorithm to handle large datasets by subsampling data. The paper reviews the theoretical foundations of variational inference, including the mean-field family and the natural gradient. It derives the stochastic variational inference algorithm, which iteratively updates variational parameters using subsampled data. The algorithm is shown to be efficient and scalable, enabling the analysis of massive data sets with complex probabilistic models. The method is applied to topic models, where it successfully approximates posterior topics from large document collections. The results demonstrate the effectiveness of stochastic variational inference in handling large-scale data and complex Bayesian models.