Accurate prediction of user behaviors is important for many social media applications, including social marketing, personalization and recommendation, etc. A major challenge lies ...
ErHeng Zhong, Wei Fan, Junwei Wang, Lei Xiao, Yong...
We propose a non-parametric Bayesian model for unsupervised semantic parsing. Following Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) d...
Background: Microarrays offer great potential as a platform for molecular diagnostics, testing clinical samples for the presence of numerous biomarkers in highly multiplexed assay...
In this paper, a novel two-tier Bayesian based method is proposed for hair segmentation. In the first tier, we construct a Bayesian model by integrating hair occurrence prior prob...
Dan Wang, Shiguang Shan, Wei Zeng, Hongming Zhang,...
Background: Identification of differentially expressed genes is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increas...
This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). Prior distributions are defined using stochastic logic programs...
We develop a framework based on Bayesian model averaging to explain how animals cope with uncertainty about contingencies in classical conditioning experiments. Traditional accoun...
Aaron C. Courville, Nathaniel D. Daw, Geoffrey J. ...
Bayesian model averaging, model selection and their approximations such as BIC are generally statistically consistent, but sometimes achieve slower rates of convergence than other...
This paper explores an inferential system for recognizing visual patterns. The system is inspired by a recent memoryprediction theory and models the high-level architecture of the...
—Recently, Bayesian probabilistic models have been used for predicting software development effort. One of the reasons for the interest in the use of Bayesian probabilistic model...
Parag C. Pendharkar, Girish H. Subramanian, James ...