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I have an itoprocess such as:

ItoProcess[{\[DifferentialD]vx[t] == -vx[t]*\[DifferentialD]t + 
  2*\[DifferentialD]w[t], \[DifferentialD]x[t] == vx[t]*\[DifferentialD]t}, 
  x[t], {{x, vx}, {x0, 0}}, t, w \[Distributed] WienerProcess[]]

I want to simulate this process 10000 time by using RandomFunction. However, I need the initial value of x (x0) to be Normal distributed such as x0 = RandomVariate[NormalDistribution[0,1]]. But the ItoProcess in Mathematica doesn't support random initial condition.

Is there any method to solve this problem?

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  • $\begingroup$ Just add the random initial condition to the whole curve after it was sampled? $\endgroup$
    – Henrik Schumacher
    Commented May 16, 2018 at 18:51
  • $\begingroup$ Thank you for helping! I think it works for random X0, but sometimes I also want the initial velocity vx0 to be random, which may not be applicable. $\endgroup$
    – Shiqi
    Commented May 17, 2018 at 6:25

2 Answers 2

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Perhaps something like this would be helpful:

proc := With[{x0 = RandomVariate[NormalDistribution[0, 1]]},
  ItoProcess[
   {\[DifferentialD]vx[t] == -vx[t]*\[DifferentialD]t + 2*\[DifferentialD]w[t], \[DifferentialD]x[t] ==  vx[t]*\[DifferentialD]t},
   x[t], {{x, vx}, {x0, 0}}, t, w \[Distributed] WienerProcess[]]
 ]

then

With[{n = 350},
  TemporalData[Nest[Join[#, {RandomFunction[proc, {0, 29, 1}]}] &, {}, n]] // ListLinePlot
 ]

Blockquote

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  • $\begingroup$ Thanks a lot for helping! Yesterday I also try to solve this by using the "Table" such as "TemporalData[Table[Randomfunction[ItoProcess[...{{x, vx}, {RandomVariate[NormalDistribution[0,1]], 0}}, t, ...],{0,3,0.001}],10000]]". I will try if the two method are both applicable! $\endgroup$
    – Shiqi
    Commented May 17, 2018 at 6:31
  • $\begingroup$ You are welcome! I haven't tried what you are suggesting but it seems plausible that it would work in principle. Please note that the amount of data points you are generating is substantial and I have doubts whether either approach will be practical when time/memory considerations are taken into account (on an old machine the estimated time to make 10000 repetitions with the specified step is approx. 15 mins and 257MB in size); perhaps you should consider the suggestions in the comments and/or the other answers as viable alternatives for the volume of your data requirements $\endgroup$
    – user42582
    Commented May 17, 2018 at 6:48
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I think this is essentially what @HenrikSchumacher suggested:

nsim = 10; 
ito = RandomFunction[ItoProcess[{\[DifferentialD]vx[t] == -vx[t]*\[DifferentialD]t + 
  2*\[DifferentialD]w[t], \[DifferentialD]x[t] == vx[t]*\[DifferentialD]t},
  x[t], {{x, vx}, {x0, 0}}, t, w \[Distributed] WienerProcess[]], {0., 5., 0.01}]
ListLinePlot[ito /. x0 -> #] & /@RandomVariate[NormalDistribution[0, 1], nsim]

10 Ito realizations

Mean[ito /. x0 -> #] & /@RandomVariate[NormalDistribution[0, 1], nsim]
(* {0.264367, 0.555122, -1.45459, 0.663994, -3.19353, -0.383676, 0.51233, -1.1766, 
    1.54617, 1.6006} *)
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  • $\begingroup$ Thanks a lot for helping! In my problem I need to derive the distribution of X at some fixed time. So I also have to use orders such as "["SliceData",t]". I will try if I can define the 10000 trajectories as a TemporalData by using your method. Thanks again! $\endgroup$
    – Shiqi
    Commented May 17, 2018 at 6:37

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