Maxima 5.47.0 Manual: Exponential Random Variable

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#### 52.2.7 Exponential Random Variable

The exponential distribution is the probablity distribution of the time between events in a process where the events occur continuously and independently at a constant average rate.

Function: pdf_exp (x,m)

  Returns the value at x of the density function of an \({\it Exponential}(m)\) random variable, with m>0.

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

  The pdf is $$ f(x; m) = \cases{ me^{-mx} & for $x \ge 0$ \cr 0 & otherwise } $$

      (%i1) load ("distrib")$

      (%i2) pdf_exp(x,m);
                               - m x
      (%o2)                m %e      unit_step(x)

  Categories:Package distrib ·

Function: cdf_exp (x,m)

  Returns the value at x of the distribution function of an \({\it Exponential}(m)\) random variable, with m>0.

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

  The cdf is $$ F(x; m) = \cases{ 1 - e^{-mx} & $x \ge 0$ \cr 0 & otherwise } $$

      (%i1) load ("distrib")$

      (%i2) cdf_exp(x,m);
                                - m x
      (%o2)              (1 - %e     ) unit_step(x)

  Categories:Package distrib ·

Function: quantile_exp (q,m)

  Returns the q-quantile of an \({\it Exponential}(m)\) random variable, with m>0; in other words, this is the inverse of `cdf_exp`. Argument q must be an element of [0,1].

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

      (%i1) load ("distrib")$

      (%i2) quantile_exp(0.56,5);
      (%o2)                  0.1641961104139661

      (%i3) quantile_exp(0.56,m);
                             0.8209805520698303
      (%o3)                  ------------------
                                     m

  Categories:Package distrib ·

Function: mean_exp (m)

  Returns the mean of an \({\it Exponential}(m)\) random variable, with m>0.

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

  The mean is $$ E[X] = {1\over m} $$

      (%i1) load ("distrib")$

      (%i2) mean_exp(m);
                                      1
      (%o2)                           -
                                      m

  Categories:Package distrib ·

Function: var_exp (m)

  Returns the variance of an \({\it Exponential}(m)\) random variable, with m>0.

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

  The variance is $$ V[X] = {1\over m^2} $$

      (%i1) load ("distrib")$

      (%i2) var_exp(m);
                                     1
      (%o2)                          --
                                      2
                                     m

  Categories:Package distrib ·

Function: std_exp (m)

  Returns the standard deviation of an \({\it Exponential}(m)\) random variable, with m>0.

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

  The standard deviation is $$ D[X] = {1\over m} $$

      (%i1) load ("distrib")$

      (%i2) std_exp(m);
                                      1
      (%o2)                           -
                                      m

  Categories:Package distrib ·

Function: skewness_exp (m)

  Returns the skewness coefficient of an \({\it Exponential}(m)\) random variable, with m>0.

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

  The skewness coefficient is $$ SK[X] = 2 $$

      (%i1) load ("distrib")$

      (%i2) skewness_exp(m);
      (%o2)                           2

  Categories:Package distrib ·

Function: kurtosis_exp (m)

  Returns the kurtosis coefficient of an \({\it Exponential}(m)\) random variable, with m>0.

  The \({\it Exponential}(m)\) random variable is equivalent to the \({\it Weibull}(1,1/m)\).

  The kurtosis coefficient is $$ KU[X] = 6 $$

      (%i1) load ("distrib")$

      (%i2) kurtosis_exp(m);
      (%o2)                           6

  Categories:Package distrib ·

Function: random_exp (m)
`random_exp` (m,k)

  Returns an \({\it Exponential}(m)\) random variate, with m>0. Calling `random_exp` with a second argument k, a random sample of size k will be simulated.

  The simulation algorithm is based on the general inverse method.

  To make use of this function, write first `load("distrib")`.

  Categories:Package distrib ·Random numbers ·

Next: Lognormal Random Variable, Previous: F Random Variable, Up: Functions and Variables for continuous distributions [Contents][Index]