[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.distributions.assign_moving_mean_variance\n\n\u003cbr /\u003e\n\n|-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/distributions/python/ops/moving_stats.py#L35-L104) |\n\nCompute exponentially weighted moving {mean,variance} of a streaming value. \n\n tf.contrib.distributions.assign_moving_mean_variance(\n mean_var, variance_var, value, decay, name=None\n )\n\nThe `value` updated exponentially weighted moving `mean_var` and\n`variance_var` are given by the following recurrence relations: \n\n variance_var = decay * (variance_var + (1-decay) * (value - mean_var)**2)\n mean_var = decay * mean_var + (1 - decay) * value\n\n| **Note:** `mean_var` is updated *after* `variance_var`, i.e., `variance_var` uses the lag-1 mean.\n\nFor derivation justification, see \\[Finch (2009; Eq. 143)\\]\\[1\\].\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------------|------------------------------------------------------------------------------------------------------------------------|\n| `mean_var` | `float`-like `Variable` representing the exponentially weighted moving mean. Same shape as `variance_var` and `value`. |\n| `variance_var` | `float`-like `Variable` representing the exponentially weighted moving variance. Same shape as `mean_var` and `value`. |\n| `value` | `float`-like `Tensor`. Same shape as `mean_var` and `variance_var`. |\n| `decay` | A `float`-like `Tensor`. The moving mean decay. Typically close to `1.`, e.g., `0.999`. |\n| `name` | Optional name of the returned operation. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|----------------|-------------------------------------------------------------------------------------|\n| `mean_var` | `Variable` representing the `value`-updated exponentially weighted moving mean. |\n| `variance_var` | `Variable` representing the `value`-updated exponentially weighted moving variance. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|-------------|------------------------------------------------------------------------------|\n| `TypeError` | if `mean_var` does not have float type `dtype`. |\n| `TypeError` | if `mean_var`, `variance_var`, `value`, `decay` have different `base_dtype`. |\n\n\u003cbr /\u003e\n\n#### References\n\n\\[1\\]: Tony Finch. Incremental calculation of weighted mean and variance.\n*Technical Report* , 2009.\n\u003chttp://people.ds.cam.ac.uk/fanf2/hermes/doc/antiforgery/stats.pdf\u003e"]]
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