Add original sources/references to Wishart.py in distributions (#86543)
@fritzo As discussed, add original sources/references to Wishart.py in distributions and corrected typos in the error messages.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86543
Approved by: https://github.com/fritzo
diff --git a/torch/distributions/wishart.py b/torch/distributions/wishart.py
index e23c61c..6d31375 100644
--- a/torch/distributions/wishart.py
+++ b/torch/distributions/wishart.py
@@ -54,8 +54,11 @@
**References**
- [1] `On equivalence of the LKJ distribution and the restricted Wishart distribution`,
- Zhenxun Wang, Yunan Wu, Haitao Chu.
+ [1] Wang, Z., Wu, Y. and Chu, H., 2018. `On equivalence of the LKJ distribution and the restricted Wishart distribution`.
+ [2] Sawyer, S., 2007. `Wishart Distributions and Inverse-Wishart Sampling`.
+ [3] Anderson, T. W., 2003. `An Introduction to Multivariate Statistical Analysis (3rd ed.)`.
+ [4] Odell, P. L. & Feiveson, A. H., 1966. `A Numerical Procedure to Generate a SampleCovariance Matrix`. JASA, 61(313):199-203.
+ [5] Ku, Y.-C. & Bloomfield, P., 2010. `Generating Random Wishart Matrices with Fractional Degrees of Freedom in OX`.
"""
arg_constraints = {
'covariance_matrix': constraints.positive_definite,
@@ -216,9 +219,9 @@
def rsample(self, sample_shape=torch.Size(), max_try_correction=None):
r"""
.. warning::
- In some cases, sampling algorithn based on Bartlett decomposition may return singular matrix samples.
+ In some cases, sampling algorithm based on Bartlett decomposition may return singular matrix samples.
Several tries to correct singular samples are performed by default, but it may end up returning
- singular matrix samples. Sigular samples may return `-inf` values in `.log_prob()`.
+ singular matrix samples. Singular samples may return `-inf` values in `.log_prob()`.
In those cases, the user should validate the samples and either fix the value of `df`
or adjust `max_try_correction` value for argument in `.rsample` accordingly.
"""
