If you're looking to learn about Markov assumptions, you've come to the right place. In this article, we'll discuss what Markov assumptions are and why they're important.

Markov assumptions are a set of mathematical assumptions that allow researchers to make predictions about the future based on past data. The name "Markov" comes from the early 20th-century mathematician Andrey Markov, who first developed the assumptions.

For example, let's say you want to predict how many people will be at the park tomorrow. To do this, you could look at how many people were at the park today and make a guess based on that. But if you want to be more accurate, you could also look at how many people were at the park yesterday, and the day before, and so on.

The more data you have, the more accurate your predictions will be. But even with a small amount of data, you can still make pretty good predictions using the Markov assumptions.

1. They can improve your decision-making.

2. Better understand how to model systems.

3. Improve forecasting accuracy.

4. Better optimize systems.

The Markov assumption is that the probability of a future state is only dependent on the current state and not on the past states. These assumptions are used in machine learning to simplify the computations and make the algorithms more efficient.