Hi all,
Can anyone give me some advice for translating this equation into code using numpy?
eta(t) = lim(dt > 0) N(0, 1/sqrt(dt)),
where N(a, b) is a Gaussian random variable of mean a and variance b**2.
This is a heuristic definition of a white noise process.
Thanks, Dan
Daniel Bliss wrote:
Hi all,
Can anyone give me some advice for translating this equation into code using numpy?
eta(t) = lim(dt > 0) N(0, 1/sqrt(dt)),
where N(a, b) is a Gaussian random variable of mean a and variance b**2.
This is a heuristic definition of a white noise process.
Thanks, Dan
You want noise with infinite variance? That doesn't make sense.
On Thu, Aug 27, 2015 at 12:51 AM Daniel Bliss daniel.p.bliss@gmail.com wrote:
Can anyone give me some advice for translating this equation into code
using numpy?
eta(t) = lim(dt > 0) N(0, 1/sqrt(dt)),
where N(a, b) is a Gaussian random variable of mean a and variance b**2.
This is a heuristic definition of a white noise process.
This is an abstract definition. How to express it in numpy will depend on what you want to do with it. The easiest and most likely thing you could want would be a time series, with N time steps dt, in which sample i is the average value of the white noise process from i*dt to (i+1)*dt. This is very easy to write in numpy:
1/np.sqrt(dt) * np.random.randn(N)
Anne
participants (3)

Anne Archibald

Daniel Bliss

Neal Becker