有关命题模型（其中每个变量都有一个独特的名字），你应该看看的概率图模型的（特别是马尔可夫网络的）。他们是非常密切相关的SAT和CSP，因为它们基本上都是一般化，但仍属于同一类的复杂＃P 。

For a propositional model (where each variable has a distinct name), you should have a look at probabilistic graphical models (in particular Markov networks). They are very closely related to SAT and CSP, since they are basically a generalization, but still fall into the same complexity class #P.

如果您有兴趣简洁，一阶重这些模型的presentation，你应该考虑的的统计关系学习或一阶概率模型的（同义词） 。在这里，模型是pssed处于抬起的形式的前$ P $。例如。以下形式的可能概率的限制，使用一些变量对象域范围过：

If you are interested in concise, first order representation of these models, you should look into statistical relational learning or first order probabilistic models (synonyms). Here, the model is expressed in a "lifted" form. E.g. possibly probabilistic constraints of the following form, using variables ranging over some object domain:

on(?x,?y) => largerThan(?y,?x)

有这些模特不依赖于产生地面模型推论的领域做的解除概率推理的。