Timeline for Entropy of random variables taking real numbers
Current License: CC BY-SA 3.0
5 events
| when toggle format | what | by | license | comment | |
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| Mar 27, 2018 at 21:16 | comment | added | Aksakal | The bin sizes are equal, which is important. So, the uniform distribution will produce the highest Shannon entropy (most uncertainty), but they dropped the negative sign from usual equation. is this the confusion? | |
| Mar 27, 2018 at 21:12 | comment | added | Ijjz | I have understood the concept of entropy. I am confused with the binning method in the paper. The author's interpretation of low entropy indeed suggests that most of the values at one timestamp are at similar levels of discreteness(i.e. not uniformly distributed). Doesn't it also imply that the signals are all high or all low to obtain low entropy? Isn't it a hard constraint on the sensor values? | |
| Mar 27, 2018 at 20:52 | comment | added | Aksakal | I'm not sure I understood your question. Take a look at my answer to an earlier question: there's more uncertainty in a uniform distribution than in a bell shaped one. Pay attention to signs in Shannon's definition and Agogino's. | |
| Mar 27, 2018 at 20:31 | comment | added | Ijjz | The paper certainly put me on the right track. I should be looking for entropy estimators like nearest neighbor based, etc. I need a clarification on this paper though. If I have understood it well, the entropy is min when all the sensor values fall under similar bins(high or low valued). This might not be always true. For me one sensor should be high and the other low in a 'normal' or 'predictable' scenario. Is this assumption valid? or have I misunderstood it? | |
| Mar 26, 2018 at 18:51 | history | answered | Aksakal | CC BY-SA 3.0 |