stats_distribution_normal

Statistical Distributions -- Normal Distribution Module

rvs_normal - normal distribution random variates

Status

Experimental

Description

A normal continuous random variate distribution, also known as Gaussian, or Gauss or Laplace-Gauss distribution. The location loc specifies the mean or expectation. The scale specifies the standard deviation.

Without argument the function returns a standard normal distributed random variate N(0,1).

With two arguments, the function returns a normal distributed random variate N(loc, scale^2). For complex arguments, the real and imaginary parts are independent of each other.

With three arguments, the function returns a rank one array of normal distributed random variates.

Note: the algorithm used for generating normal random variates is fundamentally limited to double precision.

Syntax

result = rvs_normal([loc, scale] [[, array_size]])

Class

Elemental function (passing both loc and scale).

Arguments

loc: optional argument has intent(in) and is a scalar of type real or complex.

scale: optional argument has intent(in) and is a scalar of type real or complex.

array_size: optional argument has intent(in) and is a scalar of type integer.

loc and scale arguments must be of the same type.

Return value

The result is a scalar or rank one array, with a size of array_size, and as the same type of scale and loc.

Example

program example_normal_rvs
  use stdlib_random, only: random_seed
  use stdlib_stats_distribution_normal, only: norm => rvs_normal

  implicit none
  real ::  a(2, 3, 4), b(2, 3, 4)
  complex :: loc, scale
  integer :: seed_put, seed_get

  seed_put = 1234567
  call random_seed(seed_put, seed_get)

  print *, norm()           !single standard normal random variate

! 0.563655198

  print *, norm(1.0, 2.0)
!normal random variate mu=1.0, sigma=2.0

! -0.633261681

  print *, norm(0.0, 1.0, 10)       !an array of 10 standard norml random variates

! -3.38123664E-02  -0.190365672  0.220678389  -0.424612164  -0.249541596
!  0.865260184  1.11086845  -0.328349441  1.10873628  1.27049923

  a(:, :, :) = 1.0
  b(:, :, :) = 1.0
  print *, norm(a, b)         ! a rank 3 random variates array

!0.152776539  -7.51764774E-02  1.47208166  0.180561781  1.32407105
! 1.20383692  0.123445868  -0.455737948  -0.469808221  1.60750175
! 1.05748117  0.720934749  0.407810807  1.48165631  2.31749439
! 0.414566994  3.06084275  1.86505437  1.36338580  7.26878643E-02
! 0.178585172  1.39557445  0.828021586  0.872084975

  loc = (-1.0, 2.0)
  scale = (2.0, 1.0)
  print *, norm(loc, scale)
!single complex normal random variate with real part of mu=-1, sigma=2;
  !imagainary part of mu=2.0 and sigma=1.0

! (1.22566295,2.12518454)

end program example_normal_rvs

pdf_normal - normal distribution probability density function

Status

Experimental

Description

The probability density function (pdf) of the single real variable normal distribution:

For complex varible (x + y i) with independent real x and imaginary y parts, the joint probability density function is the product of corresponding marginal pdf of real and imaginary pdf (ref. "Probability and Random Processes with Applications to Signal Processing and Communications", 2nd ed., Scott L. Miller and Donald Childers, 2012, p.197):

Syntax

result = pdf_normal(x, loc, scale)

Class

Elemental function

Arguments

x: has intent(in) and is a scalar of type real or complex.

loc: has intent(in) and is a scalar of type real or complex.

scale: has intent(in) and is a scalar of type real or complex.

All three arguments must have the same type.

Return value

The result is a scalar or an array, with a shape conformable to arguments, and as the same type of input arguments.

Example

program example_normal_pdf
  use stdlib_random, only: random_seed
  use stdlib_stats_distribution_normal, only: norm_pdf => pdf_normal, &
                                              norm => rvs_normal

  implicit none
  real :: x(3, 4, 5), a(3, 4, 5), b(3, 4, 5)
  complex :: loc, scale
  integer :: seed_put, seed_get

  seed_put = 1234567
  call random_seed(seed_put, seed_get)

  print *, norm_pdf(1.0, 0., 1.) !a probability density at 1.0 in standard normal

! 0.241970733

  print *, norm_pdf(2.0, -1.0, 2.0)
!a probability density at 2.0 with mu=-1.0 sigma=2.0

!6.47588000E-02

  x = reshape(norm(0.0, 1.0, 60), [3, 4, 5])
! standard normal random variates array

  a(:, :, :) = 0.0
  b(:, :, :) = 1.0
  print *, norm_pdf(x, a, b)  ! standard normal probability density array

!  0.340346158  0.285823315  0.398714304  0.391778737  0.389345556
!  0.364551932  0.386712372  0.274370432  0.215250477  0.378006011
!  0.215760440  0.177990928  0.278640658  0.223813817  0.356875211
!  0.285167664  0.378533930  0.390739858  0.271684974  0.138273031
!  0.135456234  0.331718773  0.398283750  0.383706540

  loc = (1.0, -0.5)
  scale = (1.0, 2.)
  print *, norm_pdf((1.5, 1.0), loc, scale)
! a complex normal probability density function at (1.5,1.0) with real part
  ! of mu=1.0, sigma=1.0 and imaginary part of mu=-0.5, sigma=2.0

! 5.30100204E-02

end program example_normal_pdf

cdf_normal - normal distribution cumulative distribution function

Status

Experimental

Description

Cumulative distribution function of the single real variable normal distribution:

For the complex variable (x + y i) with independent real x and imaginary y parts, the joint cumulative distribution function is the product of corresponding marginal cdf of real and imaginary cdf (ref. "Probability and Random Processes with Applications to Signal Processing and Communications", 2nd ed., Scott L. Miller and Donald Childers, 2012, p.197):

Syntax

result = cdf_normal(x, loc, scale)

Class

Elemental function

Arguments

x: has intent(in) and is a scalar of type real or complex.

loc: has intent(in) and is a scalar of type real or complex.

scale: has intent(in) and is a scalar of type real or complex.

All three arguments must have the same type.

Return value

The result is a scalar or an array, with a shape conformable to arguments, as the same type of input arguments.

Example

program example_norm_cdf
  use stdlib_random, only: random_seed
  use stdlib_stats_distribution_normal, only: norm_cdf => cdf_normal, &
                                              norm => rvs_normal

  implicit none
  real :: x(2, 3, 4), a(2, 3, 4), b(2, 3, 4)
  complex :: loc, scale
  integer :: seed_put, seed_get

  seed_put = 1234567
  call random_seed(seed_put, seed_get)

  print *, norm_cdf(1.0, 0.0, 1.0)  ! a standard normal cumulative at 1.0

! 0.841344714

  print *, norm_cdf(2.0, -1.0, 2.0)
! a cumulative at 2.0 with mu=-1 sigma=2

! 0.933192849

  x = reshape(norm(0.0, 1.0, 24), [2, 3, 4])
! standard normal random variates array

  a(:, :, :) = 0.0
  b(:, :, :) = 1.0
  print *, norm_cdf(x, a, b)        ! standard normal cumulative array

!  0.713505626  0.207069695  0.486513376  0.424511284  0.587328553
!  0.335559726  0.401470929  0.806552052  0.866687536  0.371323735
!  0.866228044  0.898046613  0.198435277  0.141147852  0.681565762
!  0.206268221  0.627057910  0.580759525  0.190364420  7.27325380E-02
!  7.08068311E-02  0.728241026  0.522919059  0.390097380

  loc = (1.0, 0.0)
  scale = (0.5, 1.0)
  print *, norm_cdf((0.5, -0.5), loc, scale)
!complex normal cumulative distribution at (0.5,-0.5) with real part of
  !mu=1.0, sigma=0.5 and imaginary part of mu=0.0, sigma=1.0

!4.89511043E-02

end program example_norm_cdf