Lower Bound of Divergence

1. Is lower KL divergence better?
2. Is KL divergence bounded?
3. What does large KL divergence mean?
4. What is Kullback-Leibler divergence loss?

Is lower KL divergence better?

Lower the KL divergence value, the better we have matched the true distribution with our approximation.

Is KL divergence bounded?

The work was motivated by the cost-benefit ratio proposed by Chen and Golan [1], and the less desirable property that the Kullback-Leibler (KL) divergence used in the measure is unbounded.

What does large KL divergence mean?

"...the K-L divergence represents the number of extra bits necessary to code a source whose symbols were drawn from the distribution P, given that the coder was designed for a source whose symbols were drawn from Q." Quora. and. "...it is the amount of information lost when Q is used to approximate P."

What is Kullback-Leibler divergence loss?

Kullback-Leibler Divergence. The Kullback-Leibler Divergence score, or KL divergence score, quantifies how much one probability distribution differs from another probability distribution. The KL divergence between two distributions Q and P is often stated using the following notation: KL(P || Q)