tensorflow_probability/python/stats/sample_stats.py
def variance(x, sample_axis=0, keepdims=False, name=None):
  """Estimate variance using samples.

  Given `N` samples of scalar valued random variable `X`, variance may
  be estimated as

  ```none
  Var[X] := N^{-1} sum_{n=1}^N (X_n - Xbar) Conj{(X_n - Xbar)}
  Xbar := N^{-1} sum_{n=1}^N X_n
  ```

  ```python
  x = tf.random_normal(shape=(100, 2, 3))

  # var[i, j] is the sample variance of the (i, j) batch member of x.
  var = tfp.stats.variance(x, sample_axis=0)
  ```

  Notice we divide by `N` (the numpy default), which does not create `NaN`
  when `N = 1`, but is slightly biased.

  Args:
    x:  A numeric `Tensor` holding samples.
    sample_axis: Scalar or vector `Tensor` designating axis holding samples, or
      `None` (meaning all axis hold samples).
      Default value: `0` (leftmost dimension).
    keepdims:  Boolean.  Whether to keep the sample axis as singletons.
    name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `'variance'`).

  Returns:
    var: A `Tensor` of same `dtype` as the `x`, and rank equal to
      `rank(x) - len(sample_axis)`
  """
  with tf.compat.v1.name_scope(name, 'variance', values=[x, sample_axis]):
    return covariance(
        x, y=None, sample_axis=sample_axis, event_axis=None, keepdims=keepdims)
def variance(x, sample_axis=0, keepdims=False, name=None):
  with tf.compat.v1.name_scope(name, 'variance', values=[x, sample_axis]):
    return covariance(
        x, y=None, sample_axis=sample_axis, event_axis=None, keepdims=keepdims)
def variance ( x , sample_axis = 0 , keepdims = False , name = None ) : with tf . compat . v1 . name_scope ( name , 'variance' , values = [ x , sample_axis ] ) : return covariance ( x , y = None , sample_axis = sample_axis , event_axis = None , keepdims = keepdims )
Estimate variance using samples.

  Given `N` samples of scalar valued random variable `X`, variance may
  be estimated as

  ```none
  Var[X] := N^{-1} sum_{n=1}^N (X_n - Xbar) Conj{(X_n - Xbar)}
  Xbar := N^{-1} sum_{n=1}^N X_n
  ```

  ```python
  x = tf.random_normal(shape=(100, 2, 3))

  # var[i, j] is the sample variance of the (i, j) batch member of x.
  var = tfp.stats.variance(x, sample_axis=0)
  ```

  Notice we divide by `N` (the numpy default), which does not create `NaN`
  when `N = 1`, but is slightly biased.

  Args:
    x:  A numeric `Tensor` holding samples.
    sample_axis: Scalar or vector `Tensor` designating axis holding samples, or
      `None` (meaning all axis hold samples).
      Default value: `0` (leftmost dimension).
    keepdims:  Boolean.  Whether to keep the sample axis as singletons.
    name: Python `str` name prefixed to Ops created by this function.
          Default value: `None` (i.e., `'variance'`).

  Returns:
    var: A `Tensor` of same `dtype` as the `x`, and rank equal to
      `rank(x) - len(sample_axis)`
Estimate variance using samples .