EvHyper - 5 days ago 7

C# Question

I want to understand how to use the method of Monte Carlo to search for the limit probabilities of the some system S.

For example:

`S0 S1 S2 S3`

S0 0.1 0.9 0 0

S1 0 0.2 0.3 0.5

S2 0.2 0.1 0.5 0.2

S3 0.5 0 0.4 0.1

As I understand the method we need to generate a some number (x) and then compare the probability:

`if x`

0 <= x < 0.1 => S0 -> S0

0.1 <= x < 0.9 => S0 -> S1

0.9 <= x < 0.9 => S0 -> S2

0.9 <= x < 0.9 => S0 -> S3

0.9 <= x < 1 => S0 -> S4

when S4 - limit (border)

Similarly for other states.

Following this approach I can count the number of transitions:

`static double[] SimpleMonte(double[][] a, int iter = 1)`

{

var n = a.GetLength(0);

var p =

a

.Select(x => x.Select((_, i) => x.Take(i + 1).Sum()).ToArray())

.ToArray();

Random rand = new Random();

double[] X = new double[n];

for (int x = 0; x < n; x++)

{

double count = 0;

for (int i = 0; i < iter; i++)

{

int row = x;

bool notG = true;

Console.Write("{0} -> ", row);

while (notG)

{

var e = rand.NextDouble();

Console.Write("({0})", Math.Round(e, 2));

bool ch = false;

for (int j = 0; j < n - 1; j++)

{

if (p[row][j] <= e && e < p[row][j + 1])

{

row = j + 1;

ch = true;

break;

}

}

if (!ch)

notG = false;

else

{

Console.Write("{0} -> ", row);

count++;

}

}

Console.WriteLine();

}

X[x] = count / iter;

}

return X;

}

https://dotnetfiddle.net/nJF5sm

I will be glad to hear a hint on what to do to solve this problem.

Answer

In a practical sense, the best way to find a limit of the a system like that is repeated squaring of the matrix until the entries converge. This works because it is a stochastic matrix (sum of each row equals 1). When I tried it I got an answer of:

```
S0 S1 S2 S3
0.1939252 0.2593458 0.3294393 0.2172897
```

which gives the average probabilities that you will be in a particular state.

To use a Monte Carlo method, you should generate the random numbers like you have done and keep count of the transitions. Then the average probability that you're in a certain state is

`(Amount of Transitions to State S)/(Total Transitions)`

as your Total Transitions gets large enough.

In the code you provided, if you keep increasing the size of your `iter`

variable (and it does need to be relatively large) the last four lines of your output should converge to the numbers above. I hope that helps.

In the original code there was a mistake that prevented a transition to the initial state. That is the correct version:

```
static double[] SimpleMonte(double[][] a, int iter = 10000)
{
var n = a.GetLength(0);
var p =
a
.Select(x => x.Select((_, i) => x.Take(i + 1).Sum()).ToArray())
.ToArray();
Random rand = new Random();
long count = 0;
double[] X = new double[n];
int row = rand.Next(n);
for (int i = 0; i < iter; i++)
{
var e = rand.NextDouble();
count++;
X[row]++;
if (e < p[row][0])
row = 0;
else
for (int j = 0; j < n - 1; j++)
{
if (p[row][j] <= e && e < p[row][j + 1])
{
row = j + 1;
break;
}
}
}
return X.Select(x => x / count).ToArray();
}
```

Source (Stackoverflow)

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