I\'m trying to model a signal detection problem using Sympy, and need two random variables. One with a Rayleigh distribution to model noise, and one with a Rician distribut
If you know the pdf function, it's easy to create a new distribution with sympy.stats. Take a look at the existing distributions in the sympy source. You just need to subclass SingleContinuousDistribution
and define some methods. For example, here is the normal distribution (with the docstrings removed):
class NormalDistribution(SingleContinuousDistribution):
_argnames = ('mean', 'std')
@staticmethod
def check(mean, std):
_value_check(std > 0, "Standard deviation must be positive")
def pdf(self, x):
return exp(-(x - self.mean)**2 / (2*self.std**2)) / (sqrt(2*pi)*self.std)
def sample(self):
return random.normalvariate(self.mean, self.std)
def Normal(name, mean, std):
return rv(name, NormalDistribution, (mean, std))
Yes, you can generate the Rice from chi-squared and Poisson. See any thorough Rice discussion, such as https://en.wikipedia.org/wiki/Rice_distribution:
Another case where Rice(nu,sigma) comes from the following steps:
- Generate P having a Poisson distribution with parameter (also mean, for a Poisson) lambda = nu^2 / (2*sigma^2).
- Generate X having a chi-squared distribution with 2P + 2 degrees of freedom.
- Set R = sigma * sqrt(X).