Results 1 - 10 of 5,392

Graphical models, exponential families, and variational inference

by Martin J. Wainwright, Michael I. Jordan , 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
Abstract - Cited by 819 (28 self) - Add to MetaCart
all be understood in terms of exact or approximate forms of these variational representations. The variational approach provides a complementary alternative to Markov chain Monte Carlo as a general source of approximation methods for inference in large-scale statistical models.

Stochastic Variational Inference

by Matt Hoffman, David M. Blei, Chong Wang, John Paisley - JOURNAL OF MACHINE LEARNING RESEARCH (2013, IN PRESS) , 2013
"... We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet proce ..."
Abstract - Cited by 131 (27 self) - Add to MetaCart
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet

Variational inference for Dirichlet process mixtures

by David M. Blei, Michael I. Jordan - Bayesian Analysis , 2005
"... Abstract. Dirichlet process (DP) mixture models are the cornerstone of nonparametric Bayesian statistics, and the development of Monte-Carlo Markov chain (MCMC) sampling methods for DP mixtures has enabled the application of nonparametric Bayesian methods to a variety of practical data analysis prob ..."
Abstract - Cited by 244 (27 self) - Add to MetaCart
problems. However, MCMC sampling can be prohibitively slow, and it is important to explore alternatives. One class of alternatives is provided by variational methods, a class of deterministic algorithms that convert inference problems into optimization problems (Opper and Saad 2001; Wainwright and Jordan

Black box variational inference

by Rajesh Ranganath, Sean Gerrish, David M. Blei - In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics , 2014
"... Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm gen-erally requires significant model-specific anal-ysis. These efforts can hinder and deter us from quickly developing and explorin ..."
Abstract - Cited by 19 (6 self) - Add to MetaCart
Variational inference has become a widely used method to approximate posteriors in complex latent variables models. However, deriving a variational inference algorithm gen-erally requires significant model-specific anal-ysis. These efforts can hinder and deter us from quickly developing

Automatic Variational Inference in Stan

by Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei
"... Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calcula-tions; this makes it difficult for non-experts to use. We propose an automatic varia-tional inference algorithm, automatic differentiati ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calcula-tions; this makes it difficult for non-experts to use. We propose an automatic varia-tional inference algorithm, automatic

Variational Inference for Adaptor Grammars

by Shay B. Cohen, David M. Blei, Noah A. Smith
"... Adaptor grammars extend probabilistic context-free grammars to define prior distributions over trees with “rich get richer” dynamics. Inference for adaptor grammars seeks to find parse trees for raw text. This paper describes a variational inference algorithm for adaptor grammars, providing an alter ..."
Abstract - Cited by 19 (4 self) - Add to MetaCart
Adaptor grammars extend probabilistic context-free grammars to define prior distributions over trees with “rich get richer” dynamics. Inference for adaptor grammars seeks to find parse trees for raw text. This paper describes a variational inference algorithm for adaptor grammars, providing

Variational inference in nonconjugate models

by Chong Wang, David M. Blei, Neil Lawrence - Journal of Machine Learning Research , 2013
"... Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate, the coordinate ..."
Abstract - Cited by 21 (4 self) - Add to MetaCart
Mean-field variational methods are widely used for approximate posterior inference in many probabilistic models. In a typical application, mean-field methods approximately compute the posterior with a coordinate-ascent optimization algorithm. When the model is conditionally conjugate

Variational inference for visual tracking

by Jaco Vermaak, Neil D. Lawrence, Patrick Pérez - in Conf. Computer Vision and Pattern Recog, CVPR’03 , 2003
"... The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or non-Gaussian functions, leading to analytically intractable inference. Solutions then require numerical approximation techniques, of which the particle filter is a popular choice. Particle fi ..."
Abstract - Cited by 27 (1 self) - Add to MetaCart
filters, however, degrade in performance as the dimensionality of the state space increases and the support of the likelihood decreases. As an alternative to particle filters this paper introduces a variational approximation to the tracking recursion. The variational inference is intractable in itself

Variational Inference for Machine Learning

by Shakir Mohamed
"... Variational inference is one of the tools that now lies at the heart of the modern data analysis lifecycle. Variational inference is the term used to encompass approximation techniques for the solution of intractable integrals and complex distributions and operates by transforming the hard problem o ..."
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Variational inference is one of the tools that now lies at the heart of the modern data analysis lifecycle. Variational inference is the term used to encompass approximation techniques for the solution of intractable integrals and complex distributions and operates by transforming the hard problem

Collapsed variational inference for HDP

by Yee Whye Teh , Kenichi Kurihara , Max Welling - In Advances in Neural Information Processing Systems
"... Abstract A wide variety of Dirichlet-multinomial 'topic' models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models, variational techniques have certain advantages such as easy assessment of convergence, easy op ..."
Abstract - Cited by 57 (1 self) - Add to MetaCart
Abstract A wide variety of Dirichlet-multinomial 'topic' models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models, variational techniques have certain advantages such as easy assessment of convergence, easy
Results 1 - 10 of 5,392