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Computes a running mean.

Inherits From: AutoCompositeTensor

In computation, samples can be provided individually or in chunks. A "chunk" of size M implies incorporating M samples into a single expectation computation at once, which is more efficient than one by one.

RunningMean is meant to serve general streaming expectations. For a specialized version that fits streaming over MCMC samples, see ExpectationsReducer in tfp.experimental.mcmc.

num_samples A Tensor counting the number of samples accumulated so far.
mean A Tensor broadcast-compatible with num_samples giving the current mean.



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Initialize an empty RunningMean.

shape Python Tuple or TensorShape representing the shape of incoming samples.
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.

state RunningMeanState representing a stream of no inputs.


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Update the RunningMean with a new sample.

The update formula is from Philippe Pebay (2008) [1] and is identical to that used to calculate the intermediate mean in tfp.experimental.stats.RunningCovariance and tfp.experimental.stats.RunningVariance.

new_sample Incoming Tensor sample with shape and dtype compatible with those used to form the RunningMean.
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).

mean RunningMean updated to the new sample.


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