Tensorflow 概率采样耗时较长

Question

我正在尝试使用 tfp 进行采样过程。从 beta 分布中抽取样本，并将结果作为概率输入，以从二项分布中抽取样本。运行.

花了很长时间

我应该运行这样还是有最佳方法？

'''

import tensorflow_probability as tfp
tfd = tfp.distributions

m = 100000 # sample size

### first sample from Beta distribution 
### and feed the result as probability to the binomial distribution sampling
s = tfd.Sample(
    tfd.Beta(2,2),
    sample_shape = m
)
phi = s.sample()

### Second sample from Binominal distribution 
### !!! it took forever to run...
s2 = tfd.Sample(
    tfd.Binomial(total_count=10, probs=phi),
    sample_shape = m
)

y = s2.sample() # not working well


### scipy code which works well:
from scipy import stats
m = 100000 # sample size
phi = stats.beta.rvs(2, 2, size = m)
y = stats.binom.rvs(10, phi, size = m)

'''

Answer 1

TFP 分布支持我们称为“批量形状”的概念。在这里，通过给 probs=phi 和 phi.shape = [100000]，你实际上创建了一个 100k 二项式的“批次”。然后你从中采样 100k 次，试图创建 1e10 个样本，这需要一段时间！相反，试试这个：

m = 100000
s = tfd.Sample(
    tfd.Beta(2,2),
    sample_shape = m
)
phi = s.sample()

### Second sample from Binominal distribution 
s2 = tfd.Binomial(total_count=10, probs=phi)

y = s2.sample()

或者，使用 tfd.BetaBinomial!

bb = tfd.BetaBinomial(total_count=10, concentration1=2, concentration0=2)
bb.sample(100000)

但最重要的是，看看通过 TFP 的形状语义讨论的示例笔记本：https://www.tensorflow.org/probability/examples/Understanding_TensorFlow_Distributions_Shapes

干杯！

Tensorflow 概率采样耗时较长

Tensorflow Probability Sampling Take Long Time

tensorflow

sampling

montecarlo

tensorflow-probability