Bayesian network: Extension of Markov Chain
Jun Wu · 2018
In previous chapters, we often mentioned the Markov chain. It describes a state sequence where the value of each state depends on a certain number of preceding states. For many practical problems, this model is a very rough simplification. In real life, the relationships between different things often cannot be connected with a chain; they could be intersecting and highly intricate. For example, we can see from Figure 23.1 that the relationships between cardiovascular disease and its many contributing factors are too complex to be represented by a chain.