Create¶
As we saw in Getting started, to create a MultiNorm object, you
pass a mean
vector, a covariance
matrix (both as Numpy arrays)
and optionally a list of parameter names
:
from multinorm import MultiNorm
mean = [2, 3]
covariance = [[1, 2], [3, 4]]
names = ["a", "b"]
multi_norm = MultiNorm(mean, covariance, names)
But where do these things come from?
On this page, we look at the most common scenarios.
From fit¶
TODO: show example using scipy.optimise.curve_fit`
To use multinorm
, we first need to fit some parameterised model
to obtain a best-fit parameter vector and covariance matrix.
Let’s use scipy.optimize_curve_fit to fit some data.
TODO: show example using iminuit
From points¶
TODO: show example using emcee where points come from a trace
and np.std
and np.cov
is used to get the inputs
From publication¶
TODO: show example how to take covar (or par errors) from a publication or blog post, i.e. as inputs.