Mean field variational bayes for elaborate distributions
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Summary
This work develops strategies for mean eld variational Bayes approximate inference for Bayesian hierarchical models containing elaborate distributions, particularly those having more complicated forms compared with common distributions such as those in the Normal and Gamma families.
- Type
- article
- Published
- 2011-12-01
- Cited by
- 189
- References
- 52
- Access
- Open access
- OpenAlex
- https://openalex.org/W1580282541
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:40510715
Keywords
Mathematics, Bayes' theorem, Applied mathematics, Univariate, Laplace's method
References
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- The Laplace Distribution and Generalizations
- Pattern Recognition and Machine Learning
- An Introduction to Variational Methods for Graphical Models
- WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility
- Generalized extreme value distribution with time-dependence using the AR and MA models in state space form
- Inferring Parameters and Structure of Latent Variable Models by Variational Bayes
- Expectation Propagation for approximate Bayesian inference
- Variational Bridge Regression
- Global Optimization with Polynomials and the Problem of Moments
- Conservative prior distributions for variance parameters in hierarchical models
- Auxiliary mixture sampling for parameter-driven models of time series of counts with applications to state space modelling
- Regularization of Wavelet Approximations
- Table of Integrals, Series, and Products
- Partial non-Gaussian state space
- The Bayesian Lasso
- Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution
- EXACT MEAN INTEGRATED SQUARED ERROR
- Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
- A note on Gauss—Hermite quadrature
Cited by
- Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroskedastic Errors
- Comparison of Bayesian nonparametric density estimation methods
- Supplementary: Bayesian Computation Using Design of Experiments-Based Interpolation Technique
- A comprehensive review on recent advances in Variational Bayesian inference
- Variational inference for sparse spectrum Gaussian process regression
- Bayesian Functional Generalized Additive Models with Sparsely Observed Covariates
- GPU-Accelerated Bayesian Learning and Forecasting in Simultaneous Graphical Dynamic Linear Models
- Penalized Wavelets: Embedding Wavelets into Semiparametric Regression
- Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies
- Transdimensional sequential Monte Carlo for hidden Markov models using variational Bayes - SMCVB
- Gaussian Scale Mixture Models for Robust Linear Multivariate Regression with Missing Data
- Towards Model-Driven Engineering for Big Data Analytics -- An Exploratory Analysis of Domain-Specific Languages for Machine Learning
- Variational approximations in geoadditive latent Gaussian regression: mean and quantile regression
- Real-Time Semiparametric Regression for Distributed Data Sets
- Comment: DoIt—Some Thoughts on How to Do It
- Variational Inference for Heteroscedastic Semiparametric Regression
- Simple Marginally Noninformative Prior Distributions for Covariance Matrices
- Variational inferences for partially linear additive models with variable selection
- Variational inference for count response semiparametric regression
- GENERALIZED EXTREME VALUE ADDITIVE MODEL ANALYSIS VIA MEAN FIELD VARIATIONAL BAYES
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