Title :
A comprehensive review on recent advances in Variational Bayesian inference
Author :
Sain, Rohit ; Mittal, Vikas ; Gupta, Vrinda
Author_Institution :
Dept. of ECE, Nat. Inst. of Technol., Kurukshetra, India
Abstract :
Variational Bayesian (VB) inference is the latest method for prediction of data or information in various processes. It provides a faster response with a reasonable accuracy as compared to the other methods (like Monte Carlo Markov Chain (MCMC) method). There is a large literature and work on prediction of data which deals with large amount of data. When data is missing, other methods, like MCMC, cannot be used as they require complete data for processing, while VB method provides the solution with missing data also with a very fast speed. Accuracy is the main limitation with VB method. Some algorithms are developed to overcome this limitation with some computational cost. SNVA, LSVB, SSVB and some others are the latest developed method which can be used to improve the accuracy.
Keywords :
Bayes methods; computational complexity; inference mechanisms; VB method; computational cost; data prediction; information prediction; variational Bayesian inference; Accuracy; Approximation algorithms; Bayes methods; Computational modeling; Function approximation; Inference algorithms; Gaussian mixtures; Laplace approximation; Online VB; Variational Bayesian inference; latent variables; mean field; nonparametric; skew normal; stochastic search;
Conference_Titel :
Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in
Conference_Location :
Ghaziabad
DOI :
10.1109/ICACEA.2015.7164793
