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Mean Field Variational Bayes for Elaborate Distributions
| Content Provider | Semantic Scholar |
|---|---|
| Author | Wand, Matthew P. Ormerod, John T. Padoan, Simone A. Frührwirth, Rudolf |
| Copyright Year | 2011 |
| Abstract | We develop strategies for mean field variational Bayes approximate inference for Bayesian hierarchical models containing elaborate distributions. We loosely define elaborate distributions to be those having more complicated forms compared with common distributions such as those in the Normal and Gamma families. Examples are Asymmetric Laplace, Skew Normal and Generalized Extreme Value distributions. Such models suffer from the difficulty that the parameter updates do not admit closed form solutions. We circumvent this problem through a combination of (a) specially tailored auxiliary variables, (b) univariate quadrature schemes and (c) finite mixture approximations of troublesome density functions. An accuracy assessment is conducted and the new methodology is illustrated in an application. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://works.bepress.com/matt_wand/1/download/ |
| Alternate Webpage(s) | http://www.maths.usyd.edu.au/u/jormerod/JTOpapers/MFVBEDba.pdf |
| Alternate Webpage(s) | https://opus.lib.uts.edu.au/bitstream/10453/17931/1/2010005189OK.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm Bayesian network Emoticon Inference Mean field particle methods Population Parameter Variational principle |
| Content Type | Text |
| Resource Type | Article |
