On the physics of nested Markov models: a generalized probabilistic
theory perspective
On the physics of nested Markov models: a generalized probabilistic
theory perspective
Determining potential probability distributions with a given causal graph is vital for causality studies. To bypass the difficulty in characterizing latent variables in a Bayesian network, the nested Markov model provides an elegant algebraic approach by listing exactly all the equality constraints on the observed variables. However, this algebraically motivated …