Understand how Bayesian statistics differs from classical methods and why this matters. Learn to build, interpret, and assess Bayesian models in R using the brms package. Apply Bayesian regression ...

We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative ...

The fundamental strategy for establishing a hierarchical Bayesian multilevel model is specifying prior distributions for each unknown parameter, which enables estimation for each parameter based on ...

Multilevel modeling (MLM) as well as structural equation modeling (SEM) are commonly used in social and behavioral sciences. The main advantage of MLM is that complex relationships among variables can ...

We demonstrate that hierarchical Bayesian methods are well suited to these data by presenting a ‘standard’ partially-pooled Bayesian model for multi-region cell-count data and applying it to two ...

The Bayesian Modeling for Environmental Health Workshop is a two-day intensive course of seminars and hands-on analytical sessions to provide an approachable and practical overview of concepts, ...

The major text on multilevel modelling is his book 'Multilevel Statistical Models': (2011) 4th Edition, Wiley, Chichester. Finally, in recent years, largely in collaboration with Bill Browne, James ...