It is animals and plants which lived in or near water whose remains are most likely to be preserved, for one of the necessary conditions of preservation is quick burial, and it is only in the seas and rivers, and sometimes lakes, where mud and silt have been continuously deposited, that bodies and the like can be rapidly covered over and preserved. But even in the most favorable circumstances only a small fraction of the creatures that die are preserved in this way before decay sets n or, even more likely, before scavengers eat them. After all, all creatures live by feeding on something else, whether it be plant or animals, dead or alive, and it is only by chance that such a fate is avoided. 因为一切生物都是靠吃别的生命来活命的，不管这种东西是植物还是动物，死的还是活的，因此，生物偶尔才能避免被吃掉的命运。
There are three kinds of probabilistic graph, namely (1) directed graph(Bayesian network); (2) undirected graph(Markov network); (3) hybrid graph(Chain graph). We can represent the joint distribution over a Bayesian network by chain rule, and represent a joint distribution over a Markov network as products of potentials. Then the graph is an I-map of the distribution, and we can use d-separation principles to find conditionally independent variables in the joint distribution. HMM is a typical directed graph and RBM(Restricted Boltzmann Machine) is a typical undirected graphical model. However, in real applications, sometimes we would make use of hybrid graph models. Taking CRF(Conditional Random Field) as an example. For depth estimation in stereo computer vision, CRF is a competing model, which is a hybrid model, with undirected links among depth values and directed links between depth values and features. So CRF in this case is named as Chain graph. How to represent this hybrid graph using probabilities? Usually, we use products of CRF distributions as its joint probability distribution. Then how can we determine whether two groups of variable are conditional independent or not? In the chain graph, we will use c-separation principles. By the way, chain graph is a generalization of directed graph and undirected graph. Therefore, probability representation and independence determination in Bayesian Network and Markov Network are specific cases to those in Chain Graph.
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