Bayesian joint inference for multiple directed acyclic graphs

Understand

In many applications, data often arise from multiple groups that may share similar characteristics.

• A joint estimation method that models several groups simultaneously can be more efficient than estimating parameters in each group separately.
• We focus on unraveling the dependence structures of data based on directed acyclic graphs and propose a Bayesian joint inference method for multiple graphs.
• To encourage similar dependence structures across all groups, a Markov random field prior is adopted.