There is a long run expression of Markov process which is known as **steady-state probabilities** which shows thatwhen the sample of a Markov chain cannot be reduced further. It is shown by:

Lt p_{ij}^{(n)} = ∏j (i.e., independent of i)

Where ∏j shows the steady state and is denoted by the equation:

In the above expression we see that ∏j is the steady state because in the process of finding a probability of a state say j, in the long run the transition slowly tends to return back to ∏j .

We also have an equation as:

**µ _{ij}**stands for

**Links of Previous Main Topic:-**

- Introduction to statistics
- Knowledge of central tendency or location
- Definition of dispersion
- Moments
- Bivariate distribution
- Theorem of total probability addition theorem
- Random variable
- Binomial distribution
- What is sampling
- Estimation
- Statistical hypothesis and related terms
- Analysis of variance introduction
- Definition of stochastic process

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