Markov Matrix

What is a Markov Matrix?

A Markov Matrix, also known as a stochastic matrix, is used to represent steps in a Markov chain. Each input of the Markov matrix represents the probability of an outcome. A right stochastic matrix means each row sums to 1, whereas a left stochastic matrix means each column sums to 1. In a doubly stochastic matrix, both the rows and the columns sum to 1. The Markov matrix provides a complete way to understand the probabilities of each step in a Markov chain, and is a useful tool in almost any field that requires formal analysis.


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Using Markov Matrices in Machine Learning

An example of machine learning's utilization of Markov matrices is in land use modeling. In short, it is the analysis and prediction of how human land use and development changes over time. Machine learning has been implemented in analysis of satellite maps in Zimbabwe and, using Markovian principles, is able to predict changes in the geography due to human interaction. These sequential predictions provide scientists with a better and more accurate way of modeling future outcomes.