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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.
The main difficulty is that (AFAIK) there is no exact formula for RipleyK of StraussPointProcess, as well as many others, while it is long known for PoissonPointProcess. The usual workflow is to compare RipleyK of data with RipleyK of (estimated)...
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,...