Similarities between Entropy (information theory) and Perplexity
Entropy (information theory) and Perplexity have 3 things in common (in Unionpedia): Cross entropy, Probability distribution, Random variable.
Cross entropy
In information theory, the cross entropy between two probability distributions p and q over the same underlying set of events measures the average number of bits needed to identify an event drawn from the set, if a coding scheme is used that is optimized for an "unnatural" probability distribution q, rather than the "true" distribution p. The cross entropy for the distributions p and q over a given set is defined as follows: where H(p) is the entropy of p, and D_(p \| q) is the Kullback–Leibler divergence of q from p (also known as the relative entropy of p with respect to q — note the reversal of emphasis).
Cross entropy and Entropy (information theory) · Cross entropy and Perplexity ·
Probability distribution
In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.
Entropy (information theory) and Probability distribution · Perplexity and Probability distribution ·
Random variable
In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.
Entropy (information theory) and Random variable · Perplexity and Random variable ·
The list above answers the following questions
- What Entropy (information theory) and Perplexity have in common
- What are the similarities between Entropy (information theory) and Perplexity
Entropy (information theory) and Perplexity Comparison
Entropy (information theory) has 135 relations, while Perplexity has 12. As they have in common 3, the Jaccard index is 2.04% = 3 / (135 + 12).
References
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