# Bernoulli

The Bernoulli distribution can be thought of as a coin flip, returning either `true`

or `false`

with a probability of *p*, where *p* lies on the closed interval [0, 1]. When *p == 1*, the distribution will always return `true`

, while when *p == 0*, the distribution will always return `false`

.

```
var probability = Bernoulli.FromP(0.5);
var ratio = Bernoulli.FromRatio(5, 10);
```

Internally, the probability is represented by `_p`

, a `UInt64`

. When sampling, a random `UInt64`

is generated and compared to `_p`

; if it's less than `_p`

, return `true`

, otherwise return `false`

.

This has the side effect of not allowing distributions that always return `true`

. To counteract this, *p == 1* is a special case where the RNG is not sampled, and simply always returns `true`

.

The `FromInverse`

method is provided to allow more control over the probability of the distribution. It takes a `UInt64`

and sets `_p`

directly.

```
var inverse = Bernoulli.FromInverse(UInt64.MaxValue / 2 + 1);
```

*Bernoulli Distribution* on Wikipedia

# Uniform

A uniform distribution over an interval has a *uniform* (or equal) probability of producing any value within that range. For example, the outcome of rolling a 6 sided die is represented by a uniform distribution over the interval [1, 6].

```
Uniform.Int32 d6 = Uniform.NewInclusive(1, 6);
Uniform.Int32 d20 = Uniform.NewInclusive(1, 20);
// Some may argue that there's no such thing as a perfect grade, but this may get you pretty close.
Uniform.Int32 grade = Uniform.New(0.0, 100.0);
// TimeSpans are also supported - try not to burn your popcorn.
Uniform.TimeSpan times = Uniform.NewInclusive(TimeSpan.FromMinutes(1), TimeSpan.FromMinutes(3));
```

*Continuous Uniform Distribution* on Wikipedia

*Discrete Uniform Distribution* on Wikipedia

# Unit Interval

Unit interval distributions are a special case of uniform distributions over the unit interval*, the interval from 0 to 1. Four distinct distributions are provided, closed-open, open-closed, closed-closed, and open-open.

```
using RandN.Distributions.UnitInterval;
var closedOpen = ClosedOpen.Double.Instance;
var open = Open.Double.Instance;
```

* Treating a *unit interval* as any of the four shapes over an interval from 0 to 1: `[0, 1)`

, `(0, 1]`

, `[0, 1]`

, and `(0, 1)`

# Singleton

A singleton distribution contains exactly one value, returning that value whenever it is sampled.

```
var distribution = Singleton.New("Hello, World");
Assert.Equals("Hello, World", distribution.Sample());
```