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The Model
  • Uncertainty between correlated or causal events
  • The arcs represent the conditional dependence between the events
  • Events are characterized by a discrete probability distribution. Example states: (True, False), (High, Mid, Low),...
Derive the model
  • By choosing (run -> inference) a simple Monte Carlo simulation of the model is conducted which derives the state probabilities of the events.
  • You can get more accurate results by increasing the number of samples in the Monte Carlo simulation by choosing (configure -> settings).
  • Insert evidence to those events for which you know the states
Control
  • Double click on a node to change its CPT
  • Click on a node to change its evidence
  • Drag and drop nodes for better visibility
  • Run the model (inference) for derivation
  • "Change mode" to create and change nodes and arcs
Control
  • Double click on a node to delete the node
  • Click on two nodes one after each other to connect the nodes
  • Drag and drop nodes for better visibility
  • Performance measures can be added to the discrete "named" states of the nodes in order to quantify losses, gains,... explicitly.
  • "Change mode" to specify the CPT and derive the model
Samples
© Jan Bertrand, info@princesofserendib.com, Alpha-Version