# Ito and Stratonovich Solutions of the Linear Growth Model

#### Define ItoProcess and StatonovichProcess for the SDE .

 In[1]:= Xipr = ItoProcess[\[DifferentialD]x[t] == x[t] (r \[DifferentialD]t + \[Sigma] \[DifferentialD]w[t]), x[t], {x, x0}, t, w \[Distributed] WienerProcess[]]
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 In[2]:= Xspr = StratonovichProcess[\[DifferentialD]x[t] == x[t] (r \[DifferentialD]t + \[Sigma] \[DifferentialD]w[t]), x[t], {x, x0}, t, w \[Distributed] WienerProcess[]]
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#### Find the mean and variance functions for the Ito process.

 In[3]:= X{Mean[ipr[t]], Variance[ipr[t]]} // Simplify
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#### The mean and variance functions for the Stratonovich process are different.

 In[4]:= X{Mean[spr[t]], Variance[spr[t]]} // Simplify
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#### When , the Ito solution converges to zero almost surely.

 In[5]:= XAssuming[0 < r < \[Sigma]^2/2 && \[Sigma] > 0 && x0 > 0, Limit[Probability[ 0 < \[FormalX][t] <= 1/t, \[FormalX] \[Distributed] ipr], t -> \[Infinity]]]
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#### Confirm the convergence to zero using simulations.

 In[6]:= XListLogPlot[ RandomFunction[ ipr /. {r -> 1, \[Sigma] -> 2, x0 -> 1}, {0, 20., 0.002}, 6, Method -> "KPS"], PlotRange -> All, Joined -> True, ImageSize -> 300]
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#### When , the Stratonovich solution, however, diverges almost surely.

 In[7]:= XAssuming[0 < r < \[Sigma]^2/2 && \[Sigma] > 0 && x0 > 0, Limit[Probability[\[FormalX][t] > t, \[FormalX] \[Distributed] spr], t -> \[Infinity]]]
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#### Confirm the divergence using simulations.

 In[8]:= XListLogPlot[ RandomFunction[ spr /. {r -> 1, \[Sigma] -> 2, x0 -> 1}, {0, 20., 0.002}, 6, Method -> "KPS"], PlotRange -> All, Joined -> True, ImageSize -> 300]
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