Description: By linking conceptual theories with observed data, transparent hierarchical generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, microbiome studies, and resource allocation in epidemiology. These lectures will survey some applications of transparent generative models and show how they inform experimental design, iterative model refinement, goodness-of-fit evaluation, and agent based simulation. We emphasize a modular view of generative mechanisms and discuss how they can be flexibly recombined in new problem contexts. 

Current research in generative models is currently split across several islands of activity, and we highlight opportunities lying at disciplinary intersections. We will study both coded applications that go over these ideas and the applications will be done using R. All attendees will be asked to have a recent version of R installed on their laptop so we can do hands-on activities together.

We will go over the principles of:

- hierarchical mixture models (https://www.huber.embl.de/msmb/)

- latent variables for microbiome (https://doi.org/10.6084/m9.figshare.c.6922510.v1)

- machine learning for goodness of fit

( https://github.com/krisrs1128/generative_review. )

Registration can be completed here.

Description: By linking conceptual theories with observed data, transparent hierarchical generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, microbiome studies, and resource allocation in epidemiology. These lectures will survey some applications of transparent generative models and show how they inform experimental design, iterative model refinement, goodness-of-fit evaluation, and agent based simulation. We emphasize a modular view of generative mechanisms and discuss how they can be flexibly recombined in new problem contexts. 

Current research in generative models is currently split across several islands of activity, and we highlight opportunities lying at disciplinary intersections. We will study both coded applications that go over these ideas and the applications will be done using R. All attendees will be asked to have a recent version of R installed on their laptop so we can do hands-on activities together.

We will go over the principles of:

- hierarchical mixture models (https://www.huber.embl.de/msmb/)

- latent variables for microbiome (https://doi.org/10.6084/m9.figshare.c.6922510.v1)

- machine learning for goodness of fit

( https://github.com/krisrs1128/generative_review. )

Registration can be completed here.