A Stochastic Variational Framework for Fitting and Diagnosing Generalized Linear Mixed Models
By Linda S. L. Tan, David J. Nott. 2012
26 citations recorded by the source corpus.
In stochastic variational inference, the variational Bayes objective function is optimized using stochastic gradient approximation, where gradients computed on small random subsets of data are used to approximate the true gradient over the whole data set. This enables complex models to be fit to large data sets as data can be processed in mini-batches. In this article, we extend stochastic variational inference for conjugate-exponential models to nonconjugate models and present a stochastic nonconjugate variational message passing algorithm for fitting generalized linear mixed models that is scalable to large data sets. In addition, we show that diagnostics for prior-likelihood conflict, which are useful for Bayesian model criticism, can be obtained from nonconjugate variational message passing automatically, as an alternative to simulation-based Markov chain Monte Carlo methods. Finally, we demonstrate that for moderate-sized data sets, convergence can be accelerated by using the stochastic version of nonconjugate variational message passing in the initial stage of optimization before switching to the standard version.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability
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Citations in the corpus
- Variational Inference: A Review for Statisticians (2016, neutral): “Tan and Nott (2013, 2014) applied these message-passing methods to generalized linear mixed models (and also combined them with SVI).”
- Gaussian variational approximation with sparse precision matrices (2016, methodology): “Variational methods have been considered for efficient computation in GLMMs by Ormerod and Wand (2012); Tan and Nott (2013, 2014); Lee and Wand (2016a,b), among others.”