Many traditional database’s processing schemes are batch-based with their abilities to utilize the entire information available at a time. Though, their limitations include storage (memory issues) and computational speed (often slow) for large scale applications. Another major disadvantage of the batch processing is that any small change or update in the database often requires a reevaluation using all the data at a time. This is not efficient as it is time consuming and exhausting. So, the approach seems to be a little obsolete in this new generation of fast computation. Furthermore and recently, the decrease in the cost of performing computations online promoted the increase in streaming and online-based models. In other words, new systems are taking advantage of the online setting to build models that are able to perform in real time and handle fast computations with real time updates. Traditional models could no longer scale to very large applications. So, much support has been given to online framework as these massive and nonstationary data could not keep up with the available storage. In the case of generative models, usually, the lack of flexible priors and sometimes the high complexities in the methods often hindered their performances. In addition and most importantly, many online-based models still use traditional inference approaches such as variational Bayes (VB) and Markov chain Monte Carlo (MCMC) which individually are not flexible enough as they suffer from either accuracy or efficiency. As a result, we propose in this paper, a new model that operates in online fashion with BL (Beta-Liouville) prior due to its flexibilities in topic correlation analysis. Carrying only very few parameters (compared to the generalized Dirichlet distribution, for instance), the BL is now coupled with a robust and stochastic generative process within a new hybrid inference that combines only the advantages of the VB and Gibbs sampling in the collapsed space. This insures an efficient, fast, and accurate processing. Experimental results with nonstationary datasets for face detection, image classification, and text documents processing show the merits of the new stochastic approach.