Mean-field variational approximate Bayesian inference for latent variable models

被引：40

作者：

Consonni, Guido ^{[2
]}

Marin, Jean-Michel ^{[1
]}

机构：

[1] Univ Orsay, Lab Math Bat 425, INRIA FUTURS Projects SELECT, F-91405 Orsay, France

[2] Univ Pavia, Dept Econ & Quantitat Methods, I-27100 Pavia, Italy

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2007年 / 52卷 / 02期

关键词：

bayesian inference; Bayesian probit model; Gibbs sampling; latent variable models; marginal distribution; mean-field variational methods;

D O I：

10.1016/j.csda.2006.10.028

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The ill-posed nature of missing variable models offers a challenging testing ground for new computational techniques. This is the case for the mean-field variational Bayesian inference. The behavior of this approach in the setting of the Bayesian probit model is illustrated. It is shown that the mean-field variational method always underestimates the posterior variance and, that, for small sample sizes, the mean-field variational approximation to the posterior location could be poor. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：790 / 798

页数：9

共 50 条

[41] Approximate Bayesian Inference in Semiparametric Copula Models
Grazian, Clara
Liseo, Brunero
BAYESIAN ANALYSIS, 2017, 12 (04): : 991 - 1016
[42] Transdimensional approximate Bayesian computation for inference on invasive species models with latent variables of unknown dimension
Chkrebtii, Oksana A.
Cameron, Erin K.
Campbell, David A.
Bayne, Erin M.
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2015, 86 : 97 - 110
[43] Variational Bayesian inference for network autoregression models
Lai, Wei-Ting
Chen, Ray-Bing
Chen, Ying
Koch, Thorsten
COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2022, 169
[44] VARIATIONAL BAYESIAN INFERENCE FOR PAIRWISE MARKOV MODELS
Morales, Katherine
Petetin, Yohan
2021 IEEE STATISTICAL SIGNAL PROCESSING WORKSHOP (SSP), 2021, : 251 - 255
[45] Extended Variational Message Passing for Automated Approximate Bayesian Inference
Akbayrak, Semih
Bocharov, Ivan
de Vries, Bert
ENTROPY, 2021, 23 (07)
[46] Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation
Foulds, James
Boyles, Levi
DuBois, Christopher
Smyth, Padhraic
Welling, Max
19TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (KDD'13), 2013, : 446 - 454
[47] Scalable Mean-Field Sparse Bayesian Learning
Worley, Bradley
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2019, 67 (24) : 6314 - 6326
[48] Approximate variational inference based on a finite sample of Gaussian latent variables
Gianniotis, Nikolaos
Schnoerr, Christoph
Molkenthin, Christian
Bora, Sanjay Singh
PATTERN ANALYSIS AND APPLICATIONS, 2016, 19 (02) : 475 - 485
[49] Approximate variational inference based on a finite sample of Gaussian latent variables
Nikolaos Gianniotis
Christoph Schnörr
Christian Molkenthin
Sanjay Singh Bora
Pattern Analysis and Applications, 2016, 19 : 475 - 485
[50] Spectral Latent Variable Models for perceptual inference
Kanaujia, Atul
Sminchisescu, Cristian
Metaxas, Dimitris
2007 IEEE 11TH INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS 1-6, 2007, : 142 - +

← 1 2 3 4 5 →