Bayesian networks (BN) are directed acyclic graphical (DAG) models that have
been adopted into many fields for their strengths in transparency,
interpretability, probabilistic reasoning, and causal modeling. Given a set of
data, one hurdle towards using BNs is in building the network graph from the
data that properly handles dependencies, whether correlated or causal. In this
paper, we propose an initial methodology for discovering network structures
using Tsetlin Machines.

本文提出了一种初步的探索网络结构的方法，该方法利用了 Tsetlin Machines 进行 Bayesian Networks 建模并处理相关性和因果关系。

使用 Tsetlin 机从数据中生成贝叶斯网络模型

Generating Bayesian Network Models from Data Using Tsetlin Machines

Directed acyclic graphical models, or DAG models, are widely used to
represent complex causal systems. Since the basic task of learning such a model
from data is NP-hard, a standard approach is greedy search over the space of
directed acyclic graphs or Markov equivalence classes of directed acyclic
graphs. As the space of directed acyclic graphs on $p$ nodes and the associated
space of Markov equivalence classes are both much larger than the space of
permutations, it is desirable to consider permutation-based greedy searches.
Here, we provide the first consistency guarantees, both uniform and
high-dimensional, of a greedy permutation-based search. This search corresponds
to a simplex-like algorithm operating over the edge-graph of a sub-polytope of
the permutohedron, called a DAG associahedron. Every vertex in this polytope is
associated with a directed acyclic graph, and hence with a collection of
permutations that are consistent with the directed acyclic graph ordering. A
walk is performed on the edges of the polytope maximizing the sparsity of the
associated directed acyclic graphs. We show via simulated and real data that
this permutation search is competitive with current approaches.

使用置换的贪心方法，基于一个置换多面体中的边图，在有限多步内，找到了一个对应于支配生成有向无环图原则的图结构的稀疏最小置换，该方法成功应用于有向无环图的生成。