Belief updating and learning in semi qualitative probabilistic networks

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A constructive acceleration generally occurs when a contractor is entitled to an excusable delay; the contractor requests a time extension from the owner; the owner declines to grant a time extension or grants one in an untimely manner; the owner or its agent either expressly orders completion within the original performance period or implies in a clear manner that timely completion within the original performance period is expected; and the contractor gives notice to the owner or its agent that the contractor considers this action an acceleration order.

Preliminary experiments verify the correctness and feasibility of our methods.

On the other hand, qualitative knowledge is available in many computer vision applications, and incorporating such knowledge can improve the accuracy of parameter learning.

This paper describes a general framework based on convex optimization to incorporate constraints on parameters with training data to perform Bayesian network parameter estimation.

Quantification is well known to be a major obstacle in the construction of a probabilistic network, especially when relying on human experts for this purpose.

The construction of a qualitative probabilistic network has been proposed as an initial step in a network s quantification, since the qualitative network can be used TO gain preliminary insight IN the projected networks reasoning behaviour.

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