Query ExplorerWeight MatrixPath Effect MatrixPathway Decomposerdo() vs obs() GapSensitivityPrior vs PosteriorAbductionMarkov Blanket
Why prior vs posterior matters
The prior is the population baseline — what the model expects before any case-specific information arrives. The posterior is the personalised prediction after conditioning on what is actually known. The shift between them measures the information value of the evidence.
A node that barely updates despite strong evidence signals a missing pathway in the graph. A node that updates sharply even when not directly observed reveals indirect propagation through the network. For decision-making under uncertainty, the posterior is what matters — but the prior provides essential context for judging whether an update is plausible or the evidence is genuinely surprising.
Start here
- Open the recommended scenario for this case
- Adjust observed evidence or intervention settings
- Move to a second tool without losing context
- Compare obs() versus do() where available
- Inspect paths, blankets, or CPT structure to explain the shift