The algorithms that we use every day to manipulate data assume that we have access to all the data we need. What if there’s more data than can fit on a single computer, or if accessing the data itself to do searches is expensive? If so, we can use specialized data structures that can help us “estimate” the actual value without actually computing it. In some cases, an estimate might be good enough. These data structures include the count-min sketch, bloom filters, and reservoir sampling.

A Bayesian network is a directed graph in which each node is annotated with quantitative probability information. This article covers the definition of a Bayesian network with a graphical representation, the determination of independence between variables, and the problem of finding the probability distribution of a set of query values given some observed events.