# Calculating Probability of Markov Chain State in Discrete Time

• Zaare
In summary, the conversation discusses a Markov chain with transition probability matrix and initial probability distribution. The goal is to find an expression for the probability of the event that the chain is in state 3 at least once in the first 8 steps. The solution involves converting state 3 into an absorbing state and using this to calculate the desired probability.
Zaare
Let $$\left( {X_n } \right)_{n \ge 0}$$ be a Markov chain (discrete time).

I have

$${\bf{P}} = \left[ {pij} \right]_{i,j} = \left[ {P\left( {X_1 = j|X_0 = i} \right)} \right]_{i,j}$$,

and the initial probability distribution $${\bf{p}}^{\left( 0 \right)}$$.

I need to calculate

$$P\left( {\mathop \cup \limits_{i = 0}^8 X_i = 3} \right)$$

I can use Matlab for the numerical calculations but I need to find an expression for this probability. The only expression I have been able to find consists of too many terms to be considered reasonable.
Any suggestions on how to find an expression, that at least in some way is repetitive so that the numerical calculations can be done by the computer, would be appreciated.

Hehe, nevermind, I figuered it out. If the state 3 is turned into a absorbing state, then:

$$P\left( {\bigcup\limits_{i = 0}^8 {X_i = 3} } \right) = 1 - P\left( {\bigcup\limits_{i = 0}^8 {X_i \ne 3} } \right) = 1 - \left( {1 - P\left( {X_8 = 3} \right)} \right) = P\left( {X_8 = 3} \right)$$

Last edited:

To calculate the probability of the Markov chain being in state 3 at any point in time from 0 to 8, we can use the following formula:

P(X_n = 3) = \sum_{i = 0}^{8} P(X_n = 3|X_0 = i)P(X_0 = i)

This formula takes into account all possible initial states and the probability of transitioning to state 3 after n steps. This expression is repetitive in the sense that it considers all possible initial states and their corresponding probabilities.

Using this formula, we can easily calculate the probability of the Markov chain being in state 3 at any given time without having to consider each individual step. This can be easily implemented in Matlab for numerical calculations.

Another approach would be to use the Chapman-Kolmogorov equation, which states that:

P(X_n = j|X_0 = i) = \sum_{k = 0}^{n} P(X_n = j|X_k = i)P(X_k = i)

This formula allows us to calculate the probability of being in state j at time n, given an initial state i at time 0. This can also be implemented in Matlab for numerical calculations.

In conclusion, using these formulas, we can find an expression for the probability of the Markov chain being in state 3 at any given time. It may involve some calculations, but it is a more efficient and accurate approach compared to considering each individual step.

## 1. What is a Markov Chain State?

A Markov Chain State is a state in a system that has a probability of transitioning to another state in the next time step. It is a concept used in probability and statistics to model the behavior of a system over time.

## 2. How do you calculate the probability of a Markov Chain State in discrete time?

The probability of a Markov Chain State in discrete time can be calculated by using the transition matrix, which is a square matrix that represents the probabilities of transitioning from one state to another. To find the probability of a specific state at a specific time, you can use matrix multiplication with the transition matrix and the initial state vector.

## 3. Can the probability of a Markov Chain State be greater than 1?

No, the probability of a Markov Chain State cannot be greater than 1. The values in the transition matrix represent probabilities, which must be between 0 and 1. If the calculated probability is greater than 1, it is likely that there is an error in the transition matrix or initial state vector.

## 4. How is the initial state vector determined in a Markov Chain?

The initial state vector represents the probabilities of being in each state at the beginning of the system. It can be determined through observation or by assigning equal probabilities to each state. It is important to note that the sum of all probabilities in the initial state vector must equal 1.

## 5. Can the transition matrix change over time in a Markov Chain?

Yes, the transition matrix can change over time in a Markov Chain. This is known as a time-varying Markov Chain. In this case, the probabilities of transitioning from one state to another can change at each time step. However, the transition matrix must still follow the rules of a regular Markov Chain, where the values must be between 0 and 1 and the sum of each row must equal 1.

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