# On the KL Divergence of Probability Mixtures for Belief Contraction

• Kinzang Chhogyal
• Abhaya Nayak
• Abdul Sattar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9324)

## Abstract

Probabilistic belief change is an operation that takes a probability distribution representing a belief state along with an input sentence representing some information to be accommodated or removed, and maps it to a new probability distribution. In order to choose from many such mappings possible, techniques from information theory such as the principle of minimum cross-entropy have previously been used. Central to this principle is the Kullback-Leibler (KL) divergence. In this short study, we focus on the contraction of a belief state P by a belief a, which is the process of turning the belief a into a non-belief. The contracted belief state $$P^-_a$$ can be represented as a mixture of two states: the original belief state P, and the resultant state $$P^*_{\lnot a}$$ of revising P by $$\lnot a$$. Crucial to this mixture is the mixing factor $$\epsilon$$ which determines the proportion of P and $$P^*_{\lnot a}$$ that are to be used in this process. We show that once $$\epsilon$$ is determined, the KL divergence of $$P^-_a$$ from P is given by a function whose only argument is $$\epsilon$$. We suggest that $$\epsilon$$ is not only a mixing factor but also captures relevant aspects of P and $$P^*_{\lnot a}$$ required for computing the KL divergence.

## Keywords

Belief Revision Belief State Belief Change Argumentation Framework Probability Mixture
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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