ContinuousDistributionIfc

Returns the F(x) = Pr{X <= x} where F represents the cumulative distribution function

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

Computes the complementary cumulative probability distribution function for given value of x. This is P{X > x}

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

Returns the f(x) where f represents the probability density function for the distribution. Note this is not a probability.

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.