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![The uncontrolled re-entry of a large Soviet-era Venus probe KOSMOS-482 on May 10, 2025][1] &[Wolfram Notebook][2] [1]:...
Thank you all for pointing out the issues! As for the formulas: Variance of a sample has different scaling factor, hence the difference. Use the second CentralMoment instead: ``` In[175]:= data = {1, 2, 3}; In[176]:= wd = WeightedData[data,...
Hi Dave, thank you for sharing! That's a great expansion of my post and it is so curious to find that so many subdivisions duplicate the name of the state they are in.
![Reproducing NOAA "avocado plot" and high tornado risk forecast for Jul 15, 2024 U.S. Midwest][1] &[Wolfram Notebook][2] [1]: https://community.wolfram.com//c/portal/getImageAttachment?filename=543g5sf843g.jpeg&userId=11733 [2]:...
You can do: ``` In[24]:= RipleyK[{proc4, Entity["Country", "France"]}, Range[0, 10, 1]] Out[24]= {Quantity[0., ("Miles")^2], Quantity[0., ("Miles")^2], Quantity[0., ("Miles")^2], Quantity[0., ("Miles")^2], Quantity[0., ("Miles")^2],...
Unfortunately has not been resolved yet...
just like people - even in the border-stable countries :)
Thank you for your suggestion - it is good to know that there is interest in the spatial interpolation methods.
If you want to average over realizations you need to first simulate multiple paths. `CorrelationFunction` for ensemble of paths will automatically take average: ``` proc = TransformedProcess[E^(I*b[t]), b \[Distributed] WienerProcess[], t]; ...
Time series approach: series1 = Table[{i, y1[i]}, {i, 1, 10}]; series2 = Table[{i, y2[i]}, {i, 1, 10}]; series3 = Table[{i, y3[i]}, {i, 1, 10}]; Create multivariate TemporalData with y's component and x's as times: td =...