Exponential
Models exponentially distributed random variables This distribution is commonly use to model the time between events
Parameters
The mean of the distribution, must be > 0.0
an optional label/name
Constructors
Properties
Functions
Returns an array of probabilities each representing F(x_i). The CDF is evaluated for each point in the input array x and the probabilities are returned in the returned array.
Returns the probability of being in the interval, F(upper limit) - F(lower limit) Be careful, this is Pr{lower limit < = X < = upper limit} which includes the lower limit and has implications if the distribution is discrete
Returns the Pr{x1 <= X <= x2} for the distribution. Be careful, this is Pr{x1 <= X <= x2} which includes the lower limit and has implications if the distribution is discrete
Returns the F(x) = Pr{X <= x} where F represents the cumulative distribution function
Computes the complementary cumulative probability distribution function for given value of x. This is P{X > x}
Computes the first order loss function for the function for given value of x, G1(x) = Emax(X-x,0)
Provides the inverse cumulative distribution function for the distribution
Computes x_p where P(X <= x_p) = p for the supplied array of probabilities. Requires that the values within the supplied array are in (0,1)
Assuming that the observations in the array data are from a random sample, this function computes the likelihood function. This is computed using as the sum of the log-likelihood function raised to e. Implementation may want to specify other computationally efficient formulas for this function or (most likely) the sum of the log-likelihood function.
Computes the natural log of the pdf function evaluated at x. Implementations may want to specify computationally efficient formulas for this function.
Gets the parameters
Sets the parameters
Computes the 2nd order loss function for the distribution function for given value of x, G2(x) = (1/2)Emax(X-x,0)*max(X-x-1,0)
Returns the standard deviation for the distribution as the square root of the variance if it exists
Computes the sum of the log-likelihood function evaluated at each observation in the data. Implementations may want to specify computationally efficient formulas for this function.