Future Uses

Abstract

This chapter makes a quick review of how the methods studied in this work, the Discrete and Continuous-Time Markov Chain Processes, can be applied to different fields and it explores their use con different approaches. It also examines how the applicability of these random walks can affect diverse disciplines with different impacts. The implementation of these methods can even have the option of self-learning programming.

Keywords: Algebra, Artificial Intelligence, Autonomous Decisions, Continuous Model, Deterministic Techniques, Differential System, Discrete Model, DiscreteTime Markov Chain Process, Granularity, Hidden Markov Model, Linear Algebra, Markov Chain Process, Matrix of Transition Probabilities, Matrix System, Network, Nodes, Partial Differential Equations, Patterns, Real Field, Real-Valued Functions, Stochastic Techniques, Structural Proteomics, Unsupervised Method, Vector-Valued Functions, Vertices