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test_distributions Namespace Reference


class  TestAgainstScipy
class  TestConstraintRegistry
class  TestConstraints
class  TestDistributions
class  TestDistributionShapes
class  TestJit
class  TestKL
class  TestLazyLogitsInitialization
class  TestNumericalStability
class  TestRsample
class  TestTransforms
class  TestValidation


def pairwise (Dist, params)
def is_all_nan (tensor)


 load_tests = load_tests
bool TEST_NUMPY = True
 Example = namedtuple('Example', ['Dist', 'params'])

Detailed Description

Note [Randomized statistical tests]

This note describes how to maintain tests in this file as random sources
change. This file contains two types of randomized tests:

1. The easier type of randomized test are tests that should always pass but are
   initialized with random data. If these fail something is wrong, but it's
   fine to use a fixed seed by inheriting from common.TestCase.

2. The trickier tests are statistical tests. These tests explicitly call
   set_rng_seed(n) and are marked "see Note [Randomized statistical tests]".
   These statistical tests have a known positive failure rate
   (we set failure_rate=1e-3 by default). We need to balance strength of these
   tests with annoyance of false alarms. One way that works is to specifically
   set seeds in each of the randomized tests. When a random generator
   occasionally changes (as in #4312 vectorizing the Box-Muller sampler), some
   of these statistical tests may (rarely) fail. If one fails in this case,
   it's fine to increment the seed of the failing test (but you shouldn't need
   to increment it more than once; otherwise something is probably actually

Function Documentation

def test_distributions.is_all_nan (   tensor)
Checks if all entries of a tensor is nan.

Definition at line 86 of file

def test_distributions.pairwise (   Dist,
Creates a pair of distributions `Dist` initialzed to test each element of
param with each other.

Definition at line 76 of file