a bayesian mixture model with linear regression mixing proportions
Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is to learn how complex data distributions evolve. We develop models and Bayesian learning algorithms for inferring the temporal trends of the components in a mixture model as a function of time. We show the utility of our models by applying them to the real-life problem of tracking changes in the rates of antibiotic resistance in Escherichia coli and Staphylococcus aureus. The results show that our methods can derive meaningful temporal antibiotic resistance patterns.
bayesian mixture model linear regression mixing proportion gibbs sampler