Decision-Driven Models with Probabilistic Soft Logic

In: Conference| Publication

15 Nov 2010

Workshop Paper, NIPS Workshop on Predictive Models in Personalized Medicine, 2010, Whistler, Canada
Authors: Stephen H. Bach, Matthias Broecheler, Stanley Kok, and Lise Getoor
Direct link to paper

Abstract
We introduce the concept of a decision-driven model, a probabilistic model that reasons directly over the uncertain information of interest to a decision maker. We motivate the use of these models from the perspective of personalized medicine. Decision-driven models have a number of benefits that are of particular value in this domain, such as being easily interpretable and naturally quantifying confidences in both evidence and predictions. We show how decision-driven models can easily be constructed using probabilistic soft logic, a recently introduced framework for statistical relational learning and inference which allows the specification of medical domain knowledge in concise first-order-logic rules with assigned confidence values.

The paper is presented as a poster. Follow the link for more information on decision-driven modeling in probabilistic soft logic.

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1 Response to Decision-Driven Models with Probabilistic Soft Logic

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Jong Tosic

March 12th, 2012 at 11:03 am

The genius of an good leader is always to avoid him a predicament which common sense, minus the grace of genius, can deal with successfully.
No enterprise is much more likely to succeed than one concealed from the enemy until it is ripe for execution.

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