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Found 4 papers, 3 papers with code
A generalized Bayes framework for probabilistic clustering
Several existing clustering algorithms, including k-means, can be interpreted as generalized Bayes estimators under our framework, and hence we provide a method of uncertainty quantification for these approaches.
Bayesian joint modeling of chemical structure and dose response curves
Our model also enables the prediction of chemical dose-response profiles based on chemical structure (that is, without in vivo or in vitro testing) by taking advantage of a large database of chemicals that have already been tested for toxicity in HTS programs.
Applications
Perturbed factor analysis: Improving generalizability across studies
As an alternative, we propose a class of Perturbed Factor Analysis (PFA) models that assume a common factor structure across studies after perturbing the data via multiplication by a study-specific matrix.
Methodology
Bayesian Hierarchical Factor Regression Models to Infer Cause of Death From Verbal Autopsy Data
In low-resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause-specific mortality fraction (CSMF) for populations.
Applications
