来自石榴的贝叶斯网络的样本 [英] Sample from a Bayesian network in pomegranate

问题描述

我在石榴中使用 from_samples()构建了贝叶斯网络.我可以使用 model.predict()从模型中获得最大可能的预测.我想知道是否有一种方法可以有条件地(或无条件地)从该贝叶斯网络中采样?即是否从网络中获得随机样本，而不是最大可能的预测?

I constructed a Bayesian network using from_samples() in pomegranate. I'm able to get maximally likely predictions from the model using model.predict(). I wanted to know if there is a way to sample from this Bayesian network conditionally(or unconditionally)? i.e. is there a get random samples from the network and not the maximally likely predictions?

我查看了 model.sample()，但是它引发了 NotImplementedError .

I looked at model.sample(), but it was raising NotImplementedError.

如果使用 pomegranate 无法做到这一点，那么还有哪些其他库对于Python中的贝叶斯网络很有用?

Also if this is not possible to do using pomegranate, what other libraries are great for Bayesian networks in Python?

推荐答案

model.sample() 您可以查看 PyMC ，它也支持分发混合.但是，我不知道其他任何具有类似工厂方法的工具箱，例如pomogranate中的 from_samples().

You can have a look at PyMC which supports distribution mixtures as well. However, I dont know any other toolbox with a similar factory method like from_samples() in pomogranate.

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