A Markov chain is a mathematical model that describes a system that transitions from one state to another, with the probability of each transition depending solely on the current state.
Markov chains are mathematical models that describe random processes where the next state depends only on the current state, not on the past states. This "memoryless" characteristic simplifies the analysis of complex systems, making Markov chains useful in various areas like finance, engineering, and machine learning.
In this article, we’ll discuss the different types of Markov chains, look at their properties and provide insights on the applications and assumptions.
Basic Concept of Markov Chain
A Markov chain is a type of stochastic process, but what sets it apart is something called the "memoryless" property. This means that the future behavior of the process doesn’t depend on its past, only the present state. This characteristic is known as the Markov property, and it's a crucial part of what defines a Markov chain.

Example 1: Drawing Balls Without Replacement
Imagine a scenario where you have a bag full of balls in various colors, and you randomly pull out a ball that you do not put back. Each time you take one ball the set of balls remaining changes, thereby modifying the chance of pulling out the next ball in a particular color. The next draws are dependent on the preceding ones (the leftover balls) so this situation does not comply with the Markov property.

Example 2: Drawing Balls With Replacement
Now, consider the same scenario, but after each draw, you replace the ball back into the bag. Here, the probability of drawing each color stays the same for every selection, as each pick is independent of the others. This satisfies the Markov property and is a clear example of a Markov chain because the next draw depends only on the current state (the color drawn) and not on what happened before.
Transition Matrices
A transition matrix in a Markov chain represents the probabilities of moving from one state to another over time. For a Markov chain at time t, the matrix provides the probabilities of transitioning between states. Each element in the matrix corresponds to the probability of moving from one state to another in the next time step.
Mathematically, the element at position (i, j) in the transition matrix Pt represents the probability of transitioning from state i to state j at the next step, and is denoted as:
Pt(i,j)= P(Xt+1= jXt=i)
This means that every row in the matrix sums to 1, as it represents a complete set of probabilities for all possible transitions from a given state.
Transition matrices can be multiplied to describe transitions over multiple time steps. For example, multiplying the transition matrices for time t and t+1 gives the probability of moving between states over two time steps. In general, the product of several transition matrices over multiple time periods provides the probability of moving from one state to another over that extended interval.
Markov Chain Representation
A Markov chain can be understood as a directed graph, where states are represented as points (vertices) and the transitions between them are shown as arrows (edges) with assigned probabilities. We can represent this with a transition matrix, which shows the probabilities of moving from one state to another.
Let's look at a continuous time markov chain example with two states, A and E:
 If you’re in state A, there’s a 60% chance of staying in A and a 40% chance of moving to E.
 From state E, there’s a 70% chance of moving to A and a 30% chance of staying in E.
This can be organized into a transition matrix like this:
A 
E  
A 
0.6 
0.4 
E 
0.7 
0.3 
Each row shows all possible transitions from a particular state, and the probabilities always add up to 1.
To fully describe a Markov chain, you also need an Initial State Vector, which shows the starting probabilities of being in any state. If there are N possible states, the matrix will be NxN, and the vector will be Nx1.
If you want to find the probability of moving from one state to another over several steps, you use the Nstep Transition Matrix.
Types of Markov Chain
Markov chains come in two main types, depending on how time or changes are considered: discretetime and continuoustime.

Discrete Time Markov Chains (DTMC)
In a discrete time Markov chain, changes occur at specific time intervals. Think of it like checking the state of something at fixed moments, like checking a clock every hour. The process moves between states stepbystep, and the states are countable. When people refer to "Markov chains," they often mean DTMC. It’s the most common type used in modeling various systems.

Continuous Time Markov Chains (CTMC)
For continuous Markov chains, the changes happen in a flowing manner, without fixed intervals. Instead of jumping between states at specific times, the process can change at any moment, and time flows continuously. It’s like watching a river—things are always moving, but there's no set schedule for changes.
Properties of Markov Chain
Let’s explore the key properties of a Markov chain:

Irreducible
A machine learning markov chain is called irreducible when you can move from any state to any other state, whether in one step or several. This means that starting from any point in the chain, you can eventually reach every other state, even if it takes multiple transitions.

Periodic
A state is periodic if you can return to it only at specific intervals. The greatest common divisor of all the possible lengths of return paths defines its period. If the period is greater than one, the state is considered periodic.

Transient vs. Recurrent
A state is transient if there’s a chance that once you leave it, you might never come back. In contrast, if you can eventually return to the state, it’s called recurrent. So, transient states are like temporary stops, while recurrent states are those you revisit over time.

Absorbing
An absorbing state is a final destination in the Markov chain. Once you reach this state, you can’t leave. There are no outgoing transitions from it, meaning it "absorbs" the chain, locking it in place.
Markov Chain in Python
If you're learning Markov chains with Python, the first step is to choose your states. Let's use "A" and "E" as our states. To make mathematics easier, these letters can be translated into numbers.
After defining your states, the following step is to create a transition matrix. This matrix indicates how common it is to transition from one state to another. Imagine you have a matrix that indicates a 60% chance of remaining in state A, while there’s a 40% chance of moving to state E. On the flip side, if you’re in state E, there’s a 70% chance that you’ll return to state A and a 30% chance of staying in E.
With your states and transition matrix in place, it’s time to simulate a random walk. Picture this as a casual walk where you begin in one state and decide your next move based on the transition probabilities. You can choose to simulate this process for, say, 20 steps, allowing you to see where you end up along the way.
As you make each move, you’ll randomly pick your next state according to the probabilities you’ve set up. This creates a path through your Markov chain, illustrating how you transition from one state to another.
Your Markov chain's stationary distribution can help you understand it better. The distribution displays the longterm odds of being in every state. Utilizing the transition matrix as a guide, you will compute its left eigenvectors. The probability distribution that displays your likelihood of being in each state throughout time will be evident once you have normalized these data.
Markov Chain Applications
Here are some significant applications for Markov chains:

Deriving Useful Insights
Markov chains play a crucial role in the meaningful extraction of information from vast and complex data sets. A significant result is the stationary distribution, which illustrates the system's longterm behavior. This facilitates the understanding of a system's behavior over time by analysts, making it simpler to forecast future states from the current situation.

MCMC: Overcoming Challenges
MCMC stands out as a key application of Markov chains. This technique is especially useful when it comes to approximating complex probability distributions. Since calculating normalization factors directly can often be challenging, MCMC helps tackle these issues.

Practical Applications Across Fields
Aperiodic markov chain finds application in many different domains. By simulating the statistical characteristics of data sequences, they aid in data compression in information theory by enabling effective encoding. The most pertinent results are shown at the top of search results because search engines use Markov chains to rank websites based on user behavior. Additionally, in speech recognition systems, Markov chains enhance accuracy by predicting the likelihood of word sequences, enabling smoother and more reliable interactions with technology.

Relevance to Data Science
Understanding Markov chains is becoming increasingly important in the field of data science. As data sets grow larger and more complex, the ability to model and analyze them using Markov chains can provide valuable insights. For anyone looking to excel in data science, a solid grasp of Markov chains and their applications can be a significant advantage.
Markov Chain Assumptions
To use Markov chains effectively, it's important to know the basic assumptions behind them. Here are the key points:

Finite Number of States
There is a finite number of states in a system where Markov chains function. This implies that the various states or circumstances that the system may be in can be precisely defined.

Mutually Exclusive and Collectively Exhaustive States
There can only be one state at a time since the states have to be mutually exclusive. Additionally, they have to be comprehensive in the aggregate, encompassing every scenario and omitting none.

Constant Transition Probabilities
Another key assumption is that the chance of moving from one state to another stays the same over time. This means the probabilities don't change, which makes it easier to predict how the system will behave in the future.
Conclusion
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FAQs
1. What is a Markov chain used for?
Markov chains are handy for modeling systems that change from one state to another based on probabilities. You’ll find them used in finance for risk assessments, in engineering for reliability, and in machine learning for predicting trends. They help make sense of future outcomes based on current information.
2. What is an example of a Markov chain in real life?
Think about weather forecasting; that’s a great reallife example of a Markov chain. The forecast for tomorrow’s weather often relies on today’s conditions. So, if it’s sunny today, there’s a good chance tomorrow will be sunny too. This approach simplifies predictions about shortterm weather changes.
3. What is the Markov chain in NLP?
Markov chains are used in natural language processing (NLP) to assess the flow of words and sentences. They analyze word sequences to forecast what will happen next, which is essential for text generation and improving tools such as speech recognition and chatbots. It makes engaging with technology seem more natural.
4. What are the benefits of the Markov chain?
Markov chains come with several perks. They simplify complex systems and make it easy to understand how one state leads to another. You can quickly compute predictions based on current states, and they have applications in many areas, from data analysis to strategic decisionmaking. They really help clarify things!
5. What are the main characteristics of Markov chains?
Markov chains have several essential characteristics. Initially, they are composed of a fixed number of states and adhere to the Markov property, which asserts that the future state is only dependent upon the current state. States are exclusive, and the transition probabilities don't change. They are therefore simple to work with and analyze.