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| 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... |
| None of the countries bordering Poland before 1990 exist today: the fall of the Berlin Wall and USSR 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,... |