Comments (1)
I believe this is not a possible value for MI? (i.e. the lower bound should be 0)
Correct.
However, get_mi
implements the KSG estimator, which is not an unbiased estimator. The bias depends on your choice of k
, the number of samples, and the distributions of your random variables. One approach to compute (and then correct for) this bias is to permute your random samples (thus removing any relationship there might be between them), compute the KSG estimate for the randomized samples that are known to have zero mutual information, and then subtract the average from the KSG estimate for your samples before randomization.
from entropy_estimators.
Related Issues (15)
- continuous entropy with KNN HOT 21
- Support for computing entropy of a Tensor HOT 9
- Mutual information is greater than information entropy HOT 23
- Does "partial mutual information" here mean "conditional mutual information"?
- Does "partial mutual information" here mean "conditional mutual information"? HOT 2
- Multiplying the euclidian distance by 2. HOT 2
- Error occurred with "get_imin" HOT 1
- Can we truely get the joint distribution P(x,y) to calculate the H(x,y) ? HOT 4
- categorical values mutual info pls HOT 4
- Unexpected -inf entropy estimations HOT 6
- Transfer Entropy on Different Dimensions? HOT 2
- Regarding Maximal Entropy HOT 3
- Process finished with exit code -1073741571 (0xC00000FD) HOT 14
- readme import numpy as np HOT 1
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