在pgmpy的map_query中查找单次出现的概率

from pgmpy.models import BayesianModel
from pgmpy.factors.discrete import TabularCPD
from pgmpy.inference import VariableElimination

# 定义贝叶斯网络模型
model = BayesianModel([('A', 'B'), ('C', 'B'), ('C', 'D')])

# 定义变量的概率分布
cpd_a = TabularCPD('A', 2, [[0.6], [0.4]])
cpd_c = TabularCPD('C', 2, [[0.7], [0.3]])
cpd_b = TabularCPD('B', 2, [[0.1, 0.2, 0.3, 0.4], [0.9, 0.8, 0.7, 0.6]], evidence=['A', 'C'], evidence_card=[2, 2])
cpd_d = TabularCPD('D', 2, [[0.5, 0.6], [0.5, 0.4]], evidence=['C'], evidence_card=[2])

# 将概率分布添加到模型中
model.add_cpds(cpd_a, cpd_c, cpd_b, cpd_d)

# 创建推理对象
inference = VariableElimination(model)

# 执行查询
query = inference.map_query(variables=['B'], evidence={'A': 0, 'C': 1})

# 输出查询结果
print(query)