standard_cauchy [ size] Samples from standard Cauchy distribution with mode = 0
standard_exponential
[ size]
Samples from the standard exponential distribution
standard_gamma
shape[, size]
Samples from a standard gamma distribution
standard_normal
[ size]
Samples from a standard normal distribution (mean=0, stdev=1)
standard_t
df[, size]
Samples from a Student’s t distribution with df degrees of
freedom
triangular
left, mode, right[, size]
Samples from the triangular distribution
uniform
[ low, high, size]
Samples from a uniform distribution
vonmises
mu, kappa[, size]
Samples from a von Mises distribution
wald
mean, scale[, size]
Samples from a Wald, or inverse Gaussian, distribution
weibull
a[, size]
Samples from a Weibull distribution
zipf
a[, size]
Samples from a Zipf distribution
Although there is much criticism around the use of (standard) normal distributions in
finance, they are an indispensible tool and still the most widely used type of distribution,
in analytical as well as numerical applications. One reason is that many financial models
directly rest in one way or another on a normal distribution or a log-normal distribution.
Another reason is that many financial models that do not rest directly on a (log-)normal
assumption can be discretized, and therewith approximated for simulation purposes, by the
use of the normal distribution.
As an illustration, we want to visualize random draws from the following distributions:
Standard normal with mean of 0 and standard deviation of 1
Normal with mean of 100 and standard deviation of 20
Chi square with 0.5 degrees of freedom
Poisson with lambda of 1
We do this as follows:
In [ 8 ]: sample_size = 500
rn1 = npr.standard_normal(sample_size)
