# User Portlet

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![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 =...
For a symmetric random walk on integers: In[1]:= proc = RandomWalkProcess[1/2]; sample[pathLength_, numberOfPaths_] := RandomFunction[proc, {pathLength}, numberOfPaths] Simulate 30 paths of length 5000 each: In[3]:= sim =...
In[1]:= ARProcess[{Array[a, {2, 2}]}, Array[v, {2, 2}]] Out[1]= ARProcess[{{{a[1, 1], a[1, 2]}, {a[2, 1], a[2, 2]}}}, {{v[1, 1], v[1, 2]}, {v[2, 1], v[2, 2]}}] denotes AR process of order p=1, dimension 2. The coefficients of a...
There may not be "one theta to rule them all"  :) Compare LogLikelihood values of the results:[mcode]In[1]:= fun[ x_] := (1 - c)*(1/(E^((-a + x - c*x)^2/(2*b^2))*(b*Sqrt[2*Pi]))) In[2]:= dist = ProbabilityDistribution[fun[x], {x, -Infinity,...