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# Create a discrete Markov chain processΒΆ

This example details first how to create and manipulate a discrete Markov chain.

A discrete Markov chain , where is a process where discretized on the time grid such that:

The transition matrix of the process can be defined such that:

The library proposes to model it through the object *DiscreteMarkovChain* defined thanks to the origin (which can be either deterministic or uncertain), the transition matrix and the time grid.

```
from __future__ import print_function
import openturns as ot
import openturns.viewer as viewer
from matplotlib import pylab as plt
ot.Log.Show(ot.Log.NONE)
```

Define the origin

```
origin = ot.Dirac(0.0)
```

Define the transition matrix

```
transition = ot.SquareMatrix([[0.1, 0.3, 0.6], [0.7, 0.1, 0.2], [0.5, 0.3, 0.2]])
```

Define an 1-d mesh

```
tgrid = ot.RegularGrid(0.0, 1.0, 50)
```

Markov chain definition and realization

```
process = ot.DiscreteMarkovChain(origin, transition, tgrid)
real = process.getRealization()
graph = real.drawMarginal(0)
graph.setTitle('Discrete Markov chain')
view = viewer.View(graph)
```

Get several realizations

```
process.setTimeGrid(ot.RegularGrid(0.0,1.0,20))
reals = process.getSample(3)
graph = reals.drawMarginal(0)
graph.setTitle('Discrete Markov chain, 3 realizations')
view = viewer.View(graph)
```

Markov chain future 10 steps

```
future = process.getFuture(10)
graph = future.drawMarginal(0)
graph.setTitle('Markov chain future 10 steps')
view = viewer.View(graph)
```

Markov chain 3 different futures

```
futures = process.getFuture(10,3)
graph = futures.drawMarginal(0)
graph.setTitle('Three Markov chain futures, 10 steps')
view = viewer.View(graph)
plt.show()
```

**Total running time of the script:** ( 0 minutes 0.312 seconds)