Multinomial belief networks

arXiv:2311.16909v2 Announce Type: replace-cross
Abstract: A Bayesian approach to machine learning is attractive when we need to quantify uncertainty, deal with missing observations, when samples are scarce, or when the data is sparse. All of these commonly apply when analysing healthcare data. To address these analytical requirements, we propose a deep generative model for multinomial count data where both the weights and hidden units of the network are Dirichlet distributed. A Gibbs sampling procedure is formulated that takes advantage of …

abstract apply arxiv bayesian belief count cs.lg data deal generative healthcare healthcare data machine machine learning multinomial networks requirements samples stat.ap stat.ml type uncertainty