本文探讨了从线性链因子图转换到更通用因子图的方法，并介绍了相应的推断算法。此外，还讨论了两种主要的推断问题：一是基于最可能的标签预测；二是参数估计中所需的边际分布计算。
1.We simply move from using a linear-chain factor graph to a more general factor graph, and from forward-backward to more general (perhaps approximate) inference algorithms(compute Z).
2.Inference:The are two inference problems that arise. First, after we have trained the model, we often predict the labels of a new input x using the most likely labeling y* = arg max(y) p(y|x). Second, as will be seen in Chapter 4, estimation of the parameters typically requires that we compute the marginal distribution for each edge p(yt , yt−1 |x), and also the normalizing function Z(x).
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