I have a PDE that can be interpreted as basically an exit time problem for a certain stochastic process. I would like to use this to verify an analytical solution I've found. If I start the stochastic process at (x,y), then the average exit time from a certain region will be equal to the value of the solution of the PDE I care about at (x,y).(adsbygoogle = window.adsbygoogle || []).push({});

I could divide up my region into a discrete set of points and find the average for each of those. However that wastes some of the data because, say I start the process at x_{0}, y_{0}. During the next time step the position is x_{1}, y_{1}, and so if the total exit time is T then not only do I have the data point

x_{0}, y_{0}, T

but also

x_{1}, y_{1}, T - dt

However x_{1}, y_{1}is probably not exactly one of any predefined start points I defined.

So what I would like to do is take the whole list of data

{ { x_{0}, y_{0}, T}, {x_{1}, y_{1}, T-dt}, {x_{2}, y_{2}, T- 2 dt}, ... }

And apply a moving average filter. Is there a built in method for doing this in Matlab or Mathematica? I can find documentation for things like the filter2 command in Matlab, this seems to apply more to a case where your data is indexed by a discrete sets of points so that you can arrange it in for example a matrix. That's not the case here where the data is basically indexed by floats.

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# Moving average filter for MC solution of PDE

Can you offer guidance or do you also need help?

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