I am writing a simulation for a discrete stochastic dynamical system. Since the simulation is stochastic, I need to run the simulation multiple times and then average the values of each timestep. I have some simple julia code below to demonstrate the process.
My question was really about numerical stability when averaging data from say 100, 1000, 10000 simulations. The simulation is discrete and not continuous, so I don't need to worry as much about the fine grained discretization, where error accumulation really matters. But, I was not sure if there are issues with round-off error accumulating over time even for something as simple as a mean?
In the code below, I keep a running mean of some simulation output
v. So for each run through the simulation, I just take the average of the running mean and the new value--in this case a random number
rand(). Is this a numerically stable way to do this, or do I need to allocate and save each simulation to some large array or file, and then average them at the end?
n2 = 5 v = 0.0 for i in 1:n2 v = mean([v, rand()]) end
Any suggestions would really be appreciated. I am trying to find the right balance between numerical stability versus limiting unnecessary allocations of memory. Thanks.