Bayesian Doctor - for Bayesian Network and Bayesian Inference.
Apply Bayesian Inference to update beliefs upon observations from experiments.
When you have many random variables to work with, a Bayesian Network Conditional Probability Table can be very complicated and becomes fairly impractical in real life diagnostics. Therefore, you need to incorporate one observation at a time and update the beliefs accordingly. Here, the diachronic interpretation comes into play.
“Diachronic” means that something is happening over time; When we receive new data (new observation), the probability of our hypotheses changes. Diachronic Interpretation is a systematic way to update our beliefs as we get new data. In a lab-like setting, you can add as many mutually exclusive and non-exclusive hypotheses, add as many experiments and observations you want, and run a causal discovery session to find out the probability of your hypotheses. Diachronic interpretation tools can be used for various diagnostics from healthcare to daily problems.