Explaining Markov Chains
March 6, 2015
Do you know what a Markov chain is? If not read about “Markov Chains” on the Circuits of Imagination blog:
“A Markov chain is a set of transitions from one state to the next; Such that the transition from the current state to the next depends only on the current state, the previous and future states do not effect the probability of the transition. A transitions independence from future and past sates is called the Markov property.”
This boils down to Markov chains are a way to explain patterns that happen over time and were once used to document human behavior. The chains are not the best way to model human behavior, because they only exist in the present. They do not take into account past or future experiences, otherwise called “memoryless.” The chains can only rely on the action that previously occurred
Markov chains are useful to identify abnormal behavior in systems that don’t exhibit the Markov Property. How? If the system keeps making the wrong decisions based of its program, then it can be diagnosed and repaired. The post explains how the Markov chains are used in coding and provides an example to illustrate how developers can recognize them.
Whitney Grace, March 06, 2015
Sponsored by ArnoldIT.com, developer of Augmentext
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