通过 Monte Carlo Markov Chain 移植到 PyMC3 的简单贝叶斯网络

Question

我正在将Simple Bayesian Network via Monte Carlo Markov Chain 的示例从PyMC2 移植到PyMC3，它可以工作。
结果可以在GitHub上的pymc3_rain_sprinkler_grass_simple_bayesian_network.py.文件pymc3_rain_sprinkler_grass_simple_bayesian_network.py.

中找到

我想通过提供证据来扩展原始示例，例如草是湿的，然后让PyMC3 给我回答“如果草是湿的，下雨的概率是多少？”。

似乎生成的跟踪是“恒定的”，例如它不再有随机性的元素。查看要点中的pymc3_rain_sprinkler_grass_simple_bayesian_network_with_evidence.py 并执行df.drop_duplicates() 以了解我的意思。

我做错了什么？

Answer 1

我设法解决了我的问题。要点是将 testval 设置为“true”而不是“false”。它改善了将步进方法从 Metropolis 更改为 BinaryGibbsMetropolis 的情况。

参考这里是完整的解决方案。我也更新了要点。

import numpy as np
import pandas as pd
import pymc3 as pm

niter = 10000  # 10000
tune = 5000  # 5000

model = pm.Model()

with model:
    tv = [1]
    rain = pm.Bernoulli('rain', 0.2, shape=1, testval=tv)
    sprinkler_p = pm.Deterministic('sprinkler_p', pm.math.switch(rain, 0.01, 0.40))
    sprinkler = pm.Bernoulli('sprinkler', sprinkler_p, shape=1, testval=tv)
    grass_wet_p = pm.Deterministic('grass_wet_p', pm.math.switch(rain, pm.math.switch(sprinkler, 0.99, 0.80), pm.math.switch(sprinkler, 0.90, 0.0)))
    grass_wet = pm.Bernoulli('grass_wet', grass_wet_p, observed=np.array([1]), shape=1)

    trace = pm.sample(20000, step=[pm.BinaryGibbsMetropolis([rain, sprinkler])], tune=tune, random_seed=124)

# pm.traceplot(trace)

dictionary = {
              'Rain': [1 if ii[0] else 0 for ii in trace['rain'].tolist() ],
              'Sprinkler': [1 if ii[0] else 0 for ii in trace['sprinkler'].tolist() ],
              'Sprinkler Probability': [ii[0] for ii in trace['sprinkler_p'].tolist()],
              'Grass Wet Probability': [ii[0] for ii in trace['grass_wet_p'].tolist()],
              }
df = pd.DataFrame(dictionary)

p_rain = df[(df['Rain'] == 1)].shape[0] / df.shape[0]
print(p_rain)

p_sprinkler = df[(df['Sprinkler'] == 1)].shape[0] / df.shape[0]
print(p_sprinkler)