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19972022
most citedWhy Is Diagnosis Using Belief Networks Insensitive to Imprecision In Probabilities?

62 citations · 161 across the 16 of their papers we have counts for

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Showing 2013 · cs.AIShow all

9 papers · 2 filters

cs.AI2013★ 3 cited

A Logical Interpretation of Dempster-Shafer Theory, with Application to Visual Recognition

Gregory M. Provan

We formulate Dempster Shafer Belief functions in terms of Propositional Logic using the implicit notion of provability underlying Dempster Shafer Theory. Given a set of proposition…

cs.AI2013★ 1 cited

What is an Optimal Diagnosis?

David L. Poole, Gregory M. Provan

Within diagnostic reasoning there have been a number of proposed definitions of a diagnosis, and thus of the most likely diagnosis, including most probable posterior hypothesis, mo…

cs.AI2013

Dynamic Network Updating Techniques For Diagnostic Reasoning

Gregory M. Provan

A new probabilistic network construction system, DYNASTY, is proposed for diagnostic reasoning given variables whose probabilities change over time. Diagnostic reasoning is formula…

cs.AI2013

Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams

Gregory M. Provan

This paper addresses the tradeoffs which need to be considered in reasoning using probabilistic network representations, such as Influence Diagrams (IDs). In particular, we examine…

cs.AI2013★ 1 cited

Knowledge Engineering for Large Belief Networks

Malcolm Pradhan, Gregory M. Provan, Blackford Middleton +1

We present several techniques for knowledge engineering of large belief networks (BNs) based on the our experiences with a network derived from a large medical knowledge base. The…

cs.AI2013

An Experimental Comparison of Numerical and Qualitative Probabilistic Reasoning

Max Henrion, Gregory M. Provan, Brendan del Favero +1

Qualitative and infinitesimal probability schemes are consistent with the axioms of probability theory, but avoid the need for precise numerical probabilities. Using qualitative pr…