Computing Conditional Probabilities in Large Domains by Maximizing Renyi's Quadratic Entropy

Topics:
Electrical and Electronic
Tags:
Carnegie-Mellon University,
Computing,
Constraint,
Domain,
Human Resources,
Probability,
Training And Certification,
Workforce Management
Source:
Carnegie Mellon University

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Overview: This white paper dissertation discusses the methods for efficiently approximating conditional probabilities in large domains by maximizing the entropy of the distribution given a set of constraints. The constraints are constructed from conditional probabilities, typically of low-order, that can be accurately computed from the training data. By appropriately choosing the constraints, maximum entropy methods can balance the tradeoffs in errors due to bias and variance.

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Format: PDF | Size: 2,591KB | Date: Jun 2003 | Pages: 126

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