From c2efc37150762e4b360382a35b000ec02936314f Mon Sep 17 00:00:00 2001
From: Aditya Vijaykumar <vijaykumar.aditya@gmail.com>
Date: Wed, 14 Oct 2020 10:26:31 +0530
Subject: [PATCH] changed bilby.core.priors to bilby.core.prior
---
docs/prior.txt | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/docs/prior.txt b/docs/prior.txt
index f55aafe76..cea75744c 100644
--- a/docs/prior.txt
+++ b/docs/prior.txt
@@ -45,7 +45,7 @@ which provides extra functionality. For example, to sample from the prior:
.. code:: python
- >>> priors = bilby.core.priors.PriorDict()
+ >>> priors = bilby.core.prior.PriorDict()
>>> priors['a'] = bilby.prior.Uniform(minimum=0, maximum=10, name='a')
>>> priors['b'] = bilby.prior.Uniform(minimum=0, maximum=10, name='b')
>>> priors.sample()
@@ -89,7 +89,7 @@ matrix and standard deviations, e.g.:
>>> names = ['a', 'b'] # set the parameter names
>>> mu = [0., 5.] # the means of the parameters
>>> cov = [[2., 0.7], [0.7, 3.]] # the covariance matrix
- >>> mvg = bilby.core.priors.MultivariateGaussianDist(names, mus=mu, covs=cov)
+ >>> mvg = bilby.core.prior.MultivariateGaussianDist(names, mus=mu, covs=cov)
It is also possible to define a mixture model of multiple multivariate Gaussian modes of
different weights if required, e.g.:
@@ -100,7 +100,7 @@ different weights if required, e.g.:
>>> mu = [[0., 5.], [2., 7.]] # the means of the parameters
>>> cov = [[[2., 0.7], [0.7, 3.]], [[1., -0.9], [-0.9, 5.]]] # the covariance matrix
>>> weights = [0.3, 0.7] # weights of each mode
- >>> mvg = bilby.core.priors.MultivariateGaussianDist(names, mus=mu, covs=cov, nmodes=2, weights=weights)
+ >>> mvg = bilby.core.prior.MultivariateGaussianDist(names, mus=mu, covs=cov, nmodes=2, weights=weights)
The distribution can also have hard bounds on each parameter by supplying them.
--
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