Have you ever wondered how Google ranks web pages? If you have done research, you should know that Markov uses a page ranking algorithm based on the concept of networks.

This article about the introduction of Markov networks will help you understand the basic concept behind Markov networks and how to design them as solutions to real world problems.

**The following topics are covered in this blog :**

- What Is A Markov Chain?
- What Is The Markov Property?
- Understanding Markov Chains With An Example
- What Is A Transition Matrix?
- Markov Chain In Python
- Markov Chain Applications

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**What Is A Markov Chain ?**

The Markov network was first introduced in 1906 by Andrei Markov. He described the Markov networks as “A random process with random variables that vary from one state to another depending on certain assumptions and precise probability laws”.

These random variables vary from one to another, based on the important mathematical property of the Markov property.

**What Is The Markov Property ?**

The Markov property says that the estimated probability of moving to the next state of the random process depends only on the current state and time and that this is different from the previous state hierarchy.

The next possible function / state of the random process does not depend on the order of the previous states, Markov converts the networks to a memory-deficient process that depends only on the current state / function of a variable.

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**Understanding Markov Chains**

The **Markov chain** is a Markov process with different times and different state positions. Thus, the Markov chain is a unique series of states, each drawn from a particular state location (defined or not), which follows the Markov property.

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**What Is A Transition Matrix ?**

In the section above we discussed the operation of the Markov model with a simple example, and now let us understand the mathematical terms of a Markov process.

In a Markov process, we use a matrix to indicate the probabilities of transition from one state to another. This matrix is called the change or probability matrix. This is usually P.

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**Markov Chain In Python**

To better understand the Python Markov network, let’s look at an event coded as an example of the Python Markov network. When solving real-world problems, Markov often uses the library to efficiently mark networks.

However, the Markov chain encoding in Python is a great way to get started in Markov chain analysis and simulation. Hence comes the use of the Python Markov network. Let us see how to encode Python with the example of the weather forecast given in the previous section. Start by defining a simple class:

**Markov Chain Applications**

**Google Page Ranking :**The entire webpage can be considered as a**Markov model,**where each webpage can be a state, and the links or references between these pages can be considered as probability changes. So basically, no matter what webpage you start browsing, the chances of getting a particular webpage, say X is a consistent probability.**Typing word prediction :**Markov uses networks to predict upcoming words. They can be used for automatic completion and referrals.**Text Generator :**Markov networks are commonly used to create duplicate texts, create larger articles, or compile texts. It is also used in the name generators you see on the web.

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