Choosing the right random number distribution depends on the characteristics of the random numbers you need. Here are a few common distributions and their use cases:

`std::uniform_int_distribution`

- Generates random integers uniformly distributed within a specified range
- Use when you need random integers with equal probability across the range

```
#include <iostream>
#include <random>
int main() {
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_int_distribution<int> dist(1, 6);
for (int i = 0; i < 5; ++i) {
std::cout << dist(gen) << ' ';
}
}
```

`3 5 2 6 1`

`std::uniform_real_distribution`

- Generates random floating-point numbers uniformly distributed within a specified range
- Use when you need random real numbers with equal probability across the range

```
#include <iostream>
#include <random>
int main() {
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<double> dist(
0.0, 1.0);
for (int i = 0; i < 5; ++i) {
std::cout << dist(gen) << ' ';
}
}
```

`0.278498 0.534335 0.95655 0.157167 0.424719`

`std::normal_distribution`

- Generates random numbers according to a normal (Gaussian) distribution
- Use when you need random numbers centered around a mean with a specified standard deviation

```
#include <iostream>
#include <random>
int main() {
std::random_device rd;
std::mt19937 gen(rd());
std::normal_distribution<double> dist(
5.0, 2.0);
for (int i = 0; i < 5; ++i) {
std::cout << dist(gen) << ' ';
}
}
```

`6.20473 3.94903 7.31954 1.69591 4.97183`

Other distributions like `std::exponential_distribution`

, `std::poisson_distribution`

, and `std::gamma_distribution`

are available for more specific use cases.

Consider the properties of your random number requirements, such as the range, probability distribution, and data type, to select the appropriate distribution.

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This Question is from the Lesson:### Odds and Ends: 10 Useful Techniques

A quick tour of ten useful techniques in C++, covering dates, randomness, attributes and more