Abstract
The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation. We provide general conditions for obtaining optimal risk bounds for point estimates acquired from mean-field variational Bayesian inference. The conditions pertain to the existence of certain test functions for the distance metric on the parameter space and minimal assumptions on the prior. A general recipe for verification of the conditions is outlined which is broadly applicable to existing Bayesian models with or without latent variables. As illustrations, specific applications to Latent Dirichlet Allocation and Gaussian mixture models are discussed.
Original language | English (US) |
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Pages | 1579-1588 |
Number of pages | 10 |
State | Published - 2018 |
Externally published | Yes |
Event | 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain Duration: Apr 9 2018 → Apr 11 2018 |
Conference
Conference | 21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 |
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Country/Territory | Spain |
City | Playa Blanca, Lanzarote, Canary Islands |
Period | 4/9/18 → 4/11/18 |
ASJC Scopus subject areas
- Statistics and Probability
- Artificial Intelligence