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Questions and Answers
What does the Kullback-Leibler divergence measure?
What does the Kullback-Leibler divergence measure?
- How one probability distribution differs from another. (correct)
- The total probability of the distributions combined.
- The average of two probability distributions.
- The similarity between two probability distributions.
In the formula for KL divergence, what does the term $ ext{log}_2 K$ represent?
In the formula for KL divergence, what does the term $ ext{log}_2 K$ represent?
- The logarithmic transformation of the total components in the distribution. (correct)
- The number of distinct outcomes in probability distribution p.
- The scaling factor for the average probability.
- The maximum value of the probability components.
What is represented by $p_k$ in the formula?
What is represented by $p_k$ in the formula?
- The value of the k-th component in probability distribution p. (correct)
- The index of the probability distribution.
- The total probability of all components in p.
- The reference distribution value in q.
Which statement about the values of KL divergence is correct?
Which statement about the values of KL divergence is correct?
Why is $ ext{log}_2$ used in the KL divergence formula?
Why is $ ext{log}_2$ used in the KL divergence formula?
What do the two terms in the KL divergence formula collectively evaluate?
What do the two terms in the KL divergence formula collectively evaluate?
What role does the variable K play in KL divergence?
What role does the variable K play in KL divergence?
Which of the following best describes the relationship between p and q in terms of KL divergence?
Which of the following best describes the relationship between p and q in terms of KL divergence?
Flashcards
Kullback-Leibler (KL) Divergence
Kullback-Leibler (KL) Divergence
A value that indicates how much one probability distribution diverges from another. Higher KL divergence means greater difference.
Probability Distribution p
Probability Distribution p
One of the distributions being compared in the KL divergence calculation.
Probability Distribution q
Probability Distribution q
The reference distribution used to compare with the first distribution (p) in the KL divergence calculation.
k
k
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K
K
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p_k
p_k
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Summation of individual differences
Summation of individual differences
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Difference from the Maximum Distribution
Difference from the Maximum Distribution
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Study Notes
Information Theory - Kullback-Leibler Divergence
- The Kullback-Leibler Divergence (KLD) is a measure of the difference between two probability distributions
- D(p || q) is how much more likely the distribution p is than q.
- It's also called the relative entropy
- The expression
D(p || q)
represents the Kullback-Leibler divergence between distributions p and q. - The expression involves a summation over k from 0 to K-1.
p<sub>k</sub>
represents the probability of event k in distribution p.logâ‚‚ p<sub>k</sub>
represents the base-2 logarithm ofp<sub>k</sub>
logâ‚‚ K
represents the base-2 logarithm of K- K is the total number of possible events.
∑
is the summation operator.p<sub>k</sub>
is summed over all possible events from 0 to K–1 in distribution q.
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