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A running variance computation.

Inherits From: RunningCovariance, AutoCompositeTensor

This is just an alias for RunningCovariance, with the event_ndims set to 0 to compute variances.

RunningVariance is meant to serve general streaming variance needs. For a specialized version that fits streaming over MCMC samples, see VarianceReducer in tfp.experimental.mcmc.



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Returns the covariance accumulated so far.

ddof Requested dynamic degrees of freedom for the covariance calculation. For example, use ddof=0 for population covariance and ddof=1 for sample covariance. Defaults to the population covariance.

covariance An estimate of the covariance.


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Starts a RunningVariance from shape and dtype metadata.

shape Python Tuple or TensorShape representing the shape of incoming samples. This is useful to supply if the RunningVariance will be carried by a tf.while_loop, so that broadcasting does not change the shape across loop iterations.
dtype Dtype of incoming samples and the resulting statistics. By default, the dtype is tf.float32. Any integer dtypes will be cast to corresponding floats (i.e. tf.int32 will be cast to tf.float32), as intermediate calculations should be performing floating-point division.

var An empty RunningCovariance, ready for incoming samples.


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Initialize a RunningVariance object with given stats.

This allows the user to initialize knowing the mean, variance, and number of samples seen so far.

num_samples Scalar float Tensor, for number of examples already seen.
mean float Tensor, for starting mean of estimate.
variance float Tensor, for starting estimate of the variance.

RunningVariance object, with given mean and variance estimate.


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Update the RunningCovariance with a new sample.

The update formula is from Philippe Pebay (2008) [1]. This implementation supports both batched and chunked covariance computation. A "batch" is the usual parallel computation, namely a batch of size N implies N independent covariance computations, each stepping one sample (or chunk) at a time. A "chunk" of size M implies incorporating M samples into a single covariance computation at once, which is more efficient than one by one.

To further illustrate the difference between batching and chunking, consider the following example:

# treat as 3 samples from each of 5 independent vector random variables of
# shape (2,)
sample = tf.ones((3, 5, 2))
running_cov = tfp.experimental.stats.RunningCovariance.from_shape(
    (5, 2), event_ndims=1)
running_cov = running_cov.update(sample, axis=0)
final_cov = running_cov.covariance()
final_cov.shape # (5, 2, 2)

new_sample Incoming sample with shape and dtype compatible with those used to form this RunningCovariance.
axis If chunking is desired, this is an integer that specifies the axis with chunked samples. For individual samples, set this to None. By default, samples are not chunked (axis is None).

cov Newly allocated RunningCovariance updated to include new_sample.


[1]: Philippe Pebay. Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments. Technical Report SAND2008-6212, 2008.


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Returns the variance accumulated so far.

ddof Requested dynamic degrees of freedom for the variance calculation. For example, use ddof=0 for population variance and ddof=1 for sample variance. Defaults to the population variance.

variance An estimate of the variance.