16 天前 - … Using a large number of mixtures in mixture variational Bayesian phylogenetic
inference (VBPI), however, lead to significant computational overhead and slow convergence, …

22 天前 - … model has trouble explaining the relationships among variables. GWR addresses
this issue by fitting multiple regression … algorithm using mean field variational Bayes (MFVB) …

52 天前 - … Empirical FDRs and powers for logistic regressions in the small-n-and-p setting.
In the … We adopt variational Bayes (VB), which recasts the sampling problem as an optimal …

70 天前 - Background In the pursuit of finer Brain-Computer Interface commands, research
focus has shifted towards classifying EEG signals for multiple tasks. While single-joint …

84 天前 - … Enricherator to infer genome-wide enrichments from sequencing count data.
Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing …

91 天前 - … on the use of the Dirichlet variational posterior. Such variational posterior appears
to lack … This work proposes to employ a logistic-normal variational posterior, which exhibits …

135 天前 - … a robust tensor variational method for multiple input multiple output (MIMO)-…
variational Bayesian can be challenging, especially when dealing with the interactions of multiple …

246 天前 - … regression and linear mixed model with multiple random effects. It can resemble
the shape of the true effect size distributions and accounts for non-linear, … variational Bayes …

276 天前 - … for scalable Bayesian inference like variational Bayes [79]. Even with extensive
tuning and debugging, we were unable to fully eliminate HMC divergences from model …

322 天前 - … Bayesian Framework In order to quantify the uncertainty of the estimation, we construct
a Bayesian inference … Thus we use the variational distribution \(q_\phi (\textbf{z})\) with \(\…