- E. Xing, Tom M. Mitchell, Aarti Singh. 2010. 10-701 / 15-781 , Machine Learning : Homework 4. Abstract: One useful aspect of Bayesian networks is that they can make use of expert knowledge about a domain. Let’s take a (highly) simplified view of land-based ecosystems, and try to characterize all ecosystems in the world. Two of the most important aspects of an ecosystem are temperature(T) and precipition(P); an area can be either hot or cold, and either wet, moist or dry. The foundation of an ecosystem is its vegetation(V), and it is either sparse or dense, and either trees or shrubs/grasses (so 4 possibilities). More water and higher temperatures both make trees more likely and vegetation more dense. The presence grazing animals (G, boolean for whether or not there are grazing animals), like bison, depends on there being a lot of grass, but they don’t really need rain or moderate temperatures. Frogs (F, boolean for whether there are frogs), on the other hand, need lots of water and prefer to live near trees. Construct a Bayesian network to describe the relationship between these random variables.
