stheno.random module¶
-
class
stheno.random.
Normal
[source]¶ Bases:
stheno.random.RandomVector
Normal random variable.
- Parameters
mean (column vector, optional) – Mean of the distribution. Defaults to zero.
var (matrix) – Variance of the distribution.
-
property
dim
¶ Dimensionality.
-
property
dtype
¶ Data type.
-
kl
[source]¶ Compute the KL divergence with respect to another normal distribution.
- Parameters
other (
random.Normal
) – Other normal.- Returns
KL divergence.
- Return type
scalar
-
logpdf
(x)[source]¶ Compute the log-pdf.
- Parameters
x (input) – Values to compute the log-pdf of.
- Returns
- Log-pdf for every input in x. If it can be
determined that the list contains only a single log-pdf, then the list is flattened to a scalar.
- Return type
list[tensor]
-
property
m2
¶ Second moment.
-
marginal_credible_bounds
()[source]¶ Get the marginal credible region bounds.
- Returns
- A tuple containing the marginal means and marginal lower and
upper 95% central credible interval bounds.
- Return type
tuple
-
marginals
()[source]¶ Get the marginals.
- Returns
A tuple containing the marginal means and marginal variances.
- Return type
tuple
-
property
mean
¶ Mean.
-
property
mean_is_zero
¶ The mean is zero.
-
sample
(num=1, noise=None)[source]¶ Sample from the distribution.
- Parameters
num (int) – Number of samples.
noise (scalar, optional) – Variance of noise to add to the samples. Must be positive.
- Returns
Samples as rank 2 column vectors.
- Return type
tensor
-
property
var
¶ Variance.
-
w2
[source]¶ Compute the 2-Wasserstein distance with respect to another normal distribution.
- Parameters
other (
random.Normal
) – Other normal.- Returns
2-Wasserstein distance.
- Return type
scalar
-
class
stheno.random.
RandomProcess
[source]¶ Bases:
stheno.random.Random
A random process.
-
class
stheno.random.
RandomVector
[source]¶ Bases:
stheno.random.Random
A random vector.