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searching for Joint entropy 7 found (43 total)

Entropy estimation (1,374 words) [view diff] exact match in snippet view article find links to article

deep neural network (DNN) can be used to estimate the joint entropy and called Neural Joint Entropy Estimator (NJEE). Practically, the DNN is trained as

Brotli (1,724 words) [view diff] no match in snippet view article find links to article

past distances, use of move-to-front queue in entropy code selection, joint-entropy coding of literal and copy lengths, the use of graph algorithms in block

Independent component analysis (6,665 words) [view diff] exact match in snippet view article find links to article

unmixing matrix W {\displaystyle \mathbf {W} } which maximizes the joint entropy of the signals Y = g ( y ) {\displaystyle \mathbf {Y} =g(\mathbf {y}

Deep learning (17,362 words) [view diff] case mismatch in snippet view article find links to article

used to estimate the entropy of a stochastic process and called Neural Joint Entropy Estimator (NJEE). Such an estimation provides insights on the effects

Maximal information coefficient (1,084 words) [view diff] exact match in snippet view article find links to article

distributions, or in this case, bins with the same number of elements. Also, joint entropy is minimized by having a one-to-one correspondence between bins. If

Interaction information (2,417 words) [view diff] exact match in snippet view article find links to article

when the interaction information is written in terms of entropy and joint entropy, as follows: I ( X ; Y ; Z ) = ( H ( X ) + H ( Y ) + H ( Z ) ) − ( H

Estimation of distribution algorithm (4,068 words) [view diff] exact match in snippet view article find links to article

τ {\displaystyle \tau } and H ( τ ) {\displaystyle H(\tau )} is the joint entropy of the variables in τ {\displaystyle \tau } C P C = λ ∑ τ ∈ T eCGA H