Smooth, identifiable supermodels of discrete DAG models with latent variables
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models are widely used in causal inference and machine learning. We explicitly evaluate their dimension, show that they are curv...
| Hauptverfasser: | , |
|---|---|
| Format: | Journal article |
| Veröffentlicht: |
Bernoulli Society for Mathematical Statistics and Probability
2019
|