One important goal of data science is to help decision makers make better decisions. Markov logic networks (MLN) provide a useful framework for creating and implementing a decision making process to weigh alternative scenarios and can be used to more accurately forecast the future. MLNs have many real-world applications (e.g., business, public policy, finance, sports, health care, genetics, physics, economics..etc.).
Mathematician Andrey Markov contributed to the evolution of stochastic processes (memory-less property of a stochastic process), constructive mathematics and recursive function theory. His work is commonly referred to as Markov chains and Markov processes - used in probability theory and statistics. The basic idea is to model a random system that changes states according to a transition rule that only depends on the current state.