Updating Variational Bayes: Fast Sequential Posterior Inference

Nathaniel Tomasetti (), Catherine Forbes and Anastasios Panagiotelis

No 13/19, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Variational Bayesian (VB) methods usually produce posterior inference in a time frame considerably smaller than traditional Markov Chain Monte Carlo approaches. Although the VB posterior is an approximation, it has been shown to produce good parameter estimates and predicted values when a rich class of approximating distributions are considered. In this paper we propose Updating VB (UVB), a recursive algorithm used to update a sequence of VB posterior approximations in an online setting, with the computation of each posterior update requiring only the data observed since the previous update. An extension to the proposed algorithm, named UVB-IS, allows the user to trade accuracy for a substantial increase in computational speed through the use of importance sampling. The two methods and their properties are detailed in two separate simulation studies. Two empirical illustrations of the proposed UVB methods are provided, including one where a Dirichlet Process Mixture model is repeatedly updated in the context of predicting the future behaviour of vehicles on a stretch of the US Highway 101.

Keywords: importance sampling; forecasting; clustering; Dirichlet process mixture; variational inference (search for similar items in EconPapers)
JEL-codes: C11 G18 G39 (search for similar items in EconPapers)
Pages: 34
Date: 2019
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Updating Variational Bayes: Fast Sequential Posterior Inference (2020)
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