KSL-90-30
## Probabilistic Similarity Networks

**Reference: **
Heckerman, D. Probabilistic Similarity Networks. KSL, June, 1990.

**Abstract:**
We address the pragmatics of constructing normative expert systems, and
examine the influence diagram as a potential framework for representing
knowledge in such systems. We introduce an extension of the influence-diagram
representation called a similarity network. A similarity network is a tool
for constructing large and complex influence diagrams. The representation
allows a user to construct independent influence diagrams for subsets of a
given domain. A valid influence diagram for the entire domain can then be
constructed from the individual diagrams. Similarity networks represent
asymmetric forms of conditonal independence that are not represented
conveniently in an ordinary influence diagram. We discuss in detail one such
conditional independence, called subset independence, and examine how
similarity networks exploit this form of independence to facilitate the
construction of an influence diagram. Also, we investigate the assessment of
probability distributions for influence diagrams. We see that similarity
networks exploit subset independence to simplify such probability assessments.
We introduce a representation that is closely related to similarity networks,
called a partition. This representation further exploits subset independence
to simplify probability assessment. Finally, we examine a real-world
normative expert system for the diagnosis of lymph-node pathology, called
PATHFINDER. The similarity-network and partition representations played a
crucial role in the construction of this expert system.

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