# tfp.distributions.assign_log_moving_mean_exp

Compute the log of the exponentially weighted moving mean of the exp.

``````tfp.distributions.assign_log_moving_mean_exp(
log_mean_exp_var,
log_value,
decay,
name=None
)
``````

If `log_value` is a draw from a stationary random variable, this function approximates `log(E[exp(log_value)])`, i.e., a weighted log-sum-exp. More precisely, a `tf.Variable`, `log_mean_exp_var`, is updated by `log_value` using the following identity:

``````log_mean_exp_var =
= log(decay exp(log_mean_exp_var) + (1 - decay) exp(log_value))
= log(exp(log_mean_exp_var + log(decay)) + exp(log_value + log1p(-decay)))
= log_mean_exp_var
+ log(  exp(log_mean_exp_var   - log_mean_exp_var + log(decay))
+ exp(log_value - log_mean_exp_var + log1p(-decay)))
= log_mean_exp_var
+ log_sum_exp([log(decay), log_value - log_mean_exp_var + log1p(-decay)]).
``````

In addition to numerical stability, this formulation is advantageous because `log_mean_exp_var` can be updated in a lock-free manner, i.e., using `assign_add`. (Note: the updates are not thread-safe; it's just that the update to the tf.Variable is presumed efficient due to being lock-free.)

#### Args:

• `log_mean_exp_var`: `float`-like `Variable` representing the log of the exponentially weighted moving mean of the exp. Same shape as `log_value`.
• `log_value`: `float`-like `Tensor` representing a new (streaming) observation. Same shape as `log_mean_exp_var`.
• `decay`: A `float`-like `Tensor`. The moving mean decay. Typically close to `1.`, e.g., `0.999`.
• `name`: Optional name of the returned operation.

#### Returns:

• `log_mean_exp_var`: A reference to the input 'Variable' tensor with the `log_value`-updated log of the exponentially weighted moving mean of exp.

#### Raises:

• `TypeError`: if `log_mean_exp_var` does not have float type `dtype`.
• `TypeError`: if `log_mean_exp_var`, `log_value`, `decay` have different `base_dtype`.