## Distributions

The Chi language has three kinds of distributions:

- Constant distributions, distributions returning always the same value

- Discrete distributions, distributions returning a boolean or integer value

- Continuous distributions, distributions returning a real number value

The constant distributions are used during creation of the Chi program. Before adding stochastic behavior, you want to make sure the program itself is correct. It is much easier to verify correctness without stochastic behavior, but if you have to change the program again after the verification, you may introduce new errors in the process.

The constant distributions solve this by allowing you to program with stochastic sampling in the code, but it is not doing anything (since you get the same predictable value on each sample operation). After verifying correctness of the program, you only need to modify the distributions that you use to get proper stochastic behavior.

### Constant distributions

The constant distributions have very predictable samples, which makes them ideal for testing functioning of the program before adding stochastic behavior.

- `dist` `bool` constant`(bool b)`

  Distribution always returning `b`.

  Range | `b`
  Mean | `b`
  Variance | -

- `dist` `int` constant`(int i)`

  Distribution always returning `i`.

  Range | `i`
  Mean | `i`
  Variance | -

- `dist` `real` constant`(real r)`

  Distribution always returning `r`.

  Range | `r`
  Mean | `r`
  Variance | -

### Discrete distributions

The discrete distributions return integer or boolean sample values.

- `dist` `bool` bernoulli`(real p)`

  Outcome of an experiment with chance `p` `(0 <= p <= 1)`.

  Range | `{false, true}`
  Mean | `p` (where `false` is interpreted as `0`, and `true` is interpreted as `1`)
  Variance | `1 - p` (where `false` is interpreted as `0`, and `true` is interpreted as `1`)
  See also | Bernoulli(p), [Law (2007)], page 302

- `dist` `int` binomial`(int n, real p)`

  Number of successes when performing `n` experiments `(n > 0)` with chance `p` `(0 <= p <= 1)`.

  Range | `{0, 1, ..., n}`
  Mean | `n * p`
  Variance | `n * p * (1 - p)`
  See also | bin(n, p), [Law (2007)], page 304

- `dist` `int` geometric`(real p)`

  Geometric distribution, number of failures before success for an experiment with chance `p` `(0 < p <= 1)`.

  Range | `{0, 1, ...}`
  Mean | `(1 - p) / p`
  Variance | `(1 - p) / p^2`
  See also | geom(p), [Law (2007)], page 305

- `dist` `int` poisson`(real lambda)`

  Poisson distribution.

  Range | `{0, 1, ...}`
  Mean | `lambda`
  Variance | `lambda`
  See also | Poison(lambda), [Law (2007)], page 308

- `dist` `int` uniform`(int a, b)`

  Integer uniform distribution from `a` to `b` excluding the upper bound.

  Range | `{a, a+1, ..., b-1}`
  Mean | `(a + b - 1) / 2`
  Variance | `((b - a)^2 - 1) / 12`
  See also | DU(a, b - 1), [Law (2007)], page 303

### Continuous distributions

- `dist` `real` beta`(real p, q)`

  Beta distribution with shape parameters `p` and `q`, with `p > 0` and `q > 0`.

  Range | `[0, 1]`
  Mean | `p / (p + q)`
  Variance | `p * q / ((p + q)^2 * (p + q + 1))`
  See also | Beta(p, q), [Law (2007)], page 291

- `dist` `real` erlang`(double m, int k)`

  Erlang distribution with `k` a positive integer and `m > 0`. Equivalent to `gamma(k, m / k)`.

  Mean | `m`
  Variance | `m * m / k`
  See also | ERL(m, k), [Banks (1998)], page 153

- `dist` `real` exponential`(real m)`

  (Negative) exponential distribution with mean `m`, with `m > 0`.

  Range | `[0, infinite)`
  Mean | `m`
  Variance | `m * m`
  See also | expo(m), [Law (2007)], page 283

- `dist` `real` gamma`(real a, b)`

  Gamma distribution, with shape parameter `a > 0` and scale parameter `b > 0`.

  Mean | `a * b`
  Variance | `a * b^2`

- `dist` `real` lognormal`(real m, v2)`

  Log-normal distribution.

  Range | `[0, infinite)`
  Mean | `exp(m + v2/2)`
  Variance | `exp(2*m + v2) * (exp(v2) - 1)`
  See also | N(m, v2), [Law (2007)], page 290

- `dist` `real` normal`(real m, v2)`

  Normal distribution.

  Range | `(-infinite, infinite)`
  Mean | `m`
  Variance | `v2`
  See also | N(m, v2), [Law (2007)], page 288

- `dist` `real` random`()`

  Random number generator.

  Range | `[0, 1)`
  Mean | `0.5`
  Variance | `1 / 12`

- `dist` `real` triangle`(real a, b, c)`

  Triangle distribution, with `a < b < c`.

  Range | `[a, c]`
  Mean | `(a + b + c) /3`
  Variance | `(a^2 + c^2 + b^2 - a*b - a*c - b*c) / 18`
  See also | Triangle(a, c, b), [Law (2007)], page 300

- `dist` `real` uniform`(real a, b)`

  Real uniform distribution from `a` to `b`, excluding upper bound.

  Range | `[a, b)`
  Mean | `(a + b) / 2`
  Variance | `(b - a)^2 / 12`
  See also | U(a,b), [Law (2007)], page 282, except that distribution has an inclusive upper bound.

- `dist` `real` weibull`(real a, b)`

  Weibull distribution with shape parameter `a` and scale parameter `b`, with `a > 0` and `b > 0`.

  Range | `[0, infinite)`
  Mean | `(b / a) * G(1 / a)`
  Variance | `(b^2 / a) * (2 * G(2 / a) - (1 / a) * G(1 / a)^2)` with `G(x)` the Gamma function, `G(x)` `=` integral over `t` from `0` to `infinity`, for `t^(x - 1) * exp(-t)`
  See also | Weibull(a, b), [Law (2007)], page 284

### References

- [Banks (1998)] Jerry Banks, "Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice", John Wiley & Sons, Inc., 1998, doi:10.1002/9780470172445

- [Law (2007)] Averill M. Law, "Simulation Modeling and Analysis", fourth edition, McGraw-Hill, 2007