Geometric Interpretation and Manifold Structure of Markov Matrices

In probability theory and statistics, the term Markov property refers to memoryless of a stochastic process. It is named after Russian mathematician Andrey Andreyevich Markov. Every matrix gives linear equation system, solution this system us subset $\mathbb{R}_{n}^{n}$. This paper presents new manifold structure on set matrices. addition, matrices drawable, geometrical interpretation For proof, we use one-to-one corresponding among $n \times n$ matrices, from derived property, $(n-1)$-polytopes.