Comments (1)
For continuous variables, this package is calculating the differential entropy. Unfortunately, the differential entropy can be negative, making interpretation more difficult than in the discrete case. See chapter 8 of Cover and Thomas, for example, for a discussion of how to interpret negative differential entropies. (Consider, for instance, the differential entropy for a Gaussian which is proportional to log variance. If the variance is small, you get a negative number.)
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Related Issues (20)
- mutual information between different high dimensional continuous signal HOT 7
- raises error in continuous entropy HOT 3
- Readme lacks installation instructions HOT 1
- can it be used for feature_selection.mutual_info?
- Question: on how to compute conditional mutual information against a set of features HOT 2
- Why mutual information I(x;x) is not equal to h(x)? HOT 2
- Negative mutual information after using shuffle (but correct trend) HOT 4
- Entropy does not increase with variance HOT 1
- Unexpected scaling of mutual information with variance HOT 2
- Trying hard to find how to install the package HOT 1
- How to estimate MMI HOT 4
- What are the Units of the Entropy Output? / Differential Entropy Magnitude is Wrong HOT 1
- Compute the Jensen–Shannon divergence HOT 1
- ValueError: not enough values to unpack (expected 2, got 1) in calculating the micd HOT 9
- Question on how to compute normalized mutual information for discrete and continuous data HOT 2
- Unexpected behaviour in the mutual information calculation? HOT 2
- CMI HOT 1
- It doesn't work HOT 1
- Best way to compute mutual information in high dimension when all but one variable are iid HOT 3
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