[英]Trying to understand tensorflow bayesflow monte carlo
I apologize for the nature of this question but I'm relatively new to tensorflow. 对于这个问题的性质,我深表歉意,但是对于tensorflow我还是比较陌生。
I am having trouble understanding the bayesflow monte carlo operations of tensorflow, as described here 我无法理解tensorflow的bayesflow蒙特卡洛操作,描述在这里
As far as I know, it is an op for estimating the expected outcome of a function(?). 据我所知,这是估计函数预期结果的一种方法。
Additionally, how would I use it? 此外,我将如何使用它?
The BayesFlow Monte Carlo you are referring to is used to compute the the Monte Carlo approximization of E_p(f(Z))
, which is the expected value of a function of a random variable Z . 您所引用的BayesFlow蒙特卡洛用于计算
E_p(f(Z))
蒙特卡洛近似值,它是随机变量Z的函数的期望值。 The important part you seem to miss is that Z is a RV and that its distribution is not fully known (parameterized distr.), therefore you need to estimate. 您似乎想念的重要部分是Z是RV,并且其分布尚不完全清楚(参数化分布),因此需要估算。
You can use it like this: 您可以像这样使用它:
tf.contrib.bayesflow.monte_carlo.expectation(
f,
samples,
log_prob=None,
use_reparametrization=True,
axis=0,
keep_dims=False,
name=None
)
and for additional infos about the parameters check this . 有关参数的其他信息,请检查this 。
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