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Since the PDF can be computed in closed form you might have some luck with ProbabilityDistribution and some half-way reasonable starting values. Generate the data... mix[p_, m1_, m2_, s_] := MixtureDistribution[{p, 1 - p}, {NormalDistribution[m1, s], NormalDistribution[m2, s]}]; mixdatSum = Plus @@ RandomVariate[mix[0.75, 0.5, -1.5, 0.2], {2, 100}]; ...


0

Somebody could verify my code regarding the theory? To start getting the variance-covariance matrix I followed the code found here: Standard errors for maximum likelihood estimates in FindDistributionParameters And I elimitated the last part (Sqrt[Diagonal[cov]/len]]) as I also want to abtain the covariance matrix, so my code is: covariance[data_, dist_, ...


1

If I use exact coefficients, I get an exact answer with Integrate after a couple of minutes: GE[Theta_, A_, B_] := CopulaDistribution[{"Binormal", Theta}, {ExponentialDistribution[A], ExponentialDistribution[B]}]; Delta = 4/100; A = 10/100; B = 10; Theta = 90/100; T = 5; GExpExp[x_, s_] := PDF[GE[Theta, A, B], {x, s}] ...


1

p1 = Histogram[data, {0, 9, 1}, "Count", ChartLegends -> {"Experimental Result"}, ChartStyle -> "Pastel"]; p2 = DiscretePlot[ Length@data*PDF[PoissonDistribution[2.2766917293233084`], x], {x, 0, 10}, PlotStyle -> {Red, Medium}, PlotLegends -> {"Theoretical Poisson"}]; Show[{p1, p2}, AxesLabel -> {"Time (s)", "Counts"}] ...



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