Just to clarify: my understanding was that the MTAIR graph will eventually be extended with conditional probability estimates, so the whole model will define a probability distribution with conditional independences compatible with the underlying graph. This would make it a Bayesian Network in my eyes. However, it seems that we disagree on at least one of thing here!
Is the Analytica approach more robust to missing arrows not corresponding to conditional independences than a Bayesian network? If so, I’d be curious to hear a simplified explanation for why this is so.
Analytica allows you to define algebraic or other relationships between nodes, which can be real-valued, and have more complex relationships—but it can’t propagate evidence without explicit directional dependence. That allows more flexibility—the nodes don’t need to be conditionally independent, for example, and can be indexed to different viewpoints. This also means that it can’t easily be used for lots of things that we use BNs for, since the algorithms used are really limited.
Just to clarify: my understanding was that the MTAIR graph will eventually be extended with conditional probability estimates, so the whole model will define a probability distribution with conditional independences compatible with the underlying graph. This would make it a Bayesian Network in my eyes. However, it seems that we disagree on at least one of thing here!
Is the Analytica approach more robust to missing arrows not corresponding to conditional independences than a Bayesian network? If so, I’d be curious to hear a simplified explanation for why this is so.
Analytica allows you to define algebraic or other relationships between nodes, which can be real-valued, and have more complex relationships—but it can’t propagate evidence without explicit directional dependence. That allows more flexibility—the nodes don’t need to be conditionally independent, for example, and can be indexed to different viewpoints. This also means that it can’t easily be used for lots of things that we use BNs for, since the algorithms used are really limited.