Efficient global probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events

We present a new, efficient linear programming approach to probabilistic deduction from probabilistic knowledge-bases over conjunctive events. We show that this approach enables us to solve the classical problem of probabilistic deduction along a chain of basic events in polynomial time in the length of the chain. We then elaborate how taxonomic knowledge can be exploited in our new approach for an in, creased efficiency. We also present important new results for the classical linear programming approach to probabilistic deduction under tsxonomic knowledge.

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