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在“R 中的贝叶斯网络”一书中，作者使用 Grow - Shrink 实现来学习标记数据集的结构。我应该期望 gs 函数为学习结构提供不同的输出吗？也许也许只是从等价类中学习一个结构？

让我感到困惑的是，我运行下面的代码并得到输出：

Bayesian network learned via Constraint-based methods

  model:
    [undirected graph]
  nodes:                                 5 
  arcs:                                  6 
    undirected arcs:                     6 
    directed arcs:                       0 
  average markov blanket size:           2.40 
  average neighbourhood size:            2.40 
  average branching factor:              0.00 

  learning algorithm:                    Grow-Shrink 
  conditional independence test:         Pearson's Correlation 
  alpha threshold:                       0.05 
  tests used in the learning procedure:  80

但在书中，他们声称得到了有向图

Bayesian network learned via Constraint-based methods

  model:
    [STAT][ANL|STAT] [ALG|ANL:STAT] [VECT|ALG] [MECH|VECT:ALG]
  nodes:                                 5 
  arcs:                                  6 
    undirected arcs:                     0 
    directed arcs:                       6 
  average markov blanket size:           2.40 
  average neighbourhood size:            2.40 
  average branching factor:              1.20 

  learning algorithm:                    Grow-Shrink 
  conditional independence test:         Pearson's Correlation 
  alpha threshold:                       0.05 
  tests used in the learning procedure:  32
  optimized                              TRUE

根据书中的内容，他们运行的唯一代码是

bn.gs = gs(marks)
bn.gs

我运行的代码是

library(bnlearn)
bn_gs = gs(marks)
bn_gs

可能有什么不同？

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