Truncated
Creates a truncated normal distribution over the supplied interval. The supplied mean normalMean and variance normalVariance is the mean of the not truncated normal distribution. The supplied mean must be contained within the supplied interval.
Uses the algorithm:
W ~ U(F(a), F(b))
X = F^{-1}(W)
where "a" is the lower limit and b is the upper limit, and F is the normal distribution CDF.
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 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)
Provides the inverse cumulative distribution function for the distribution
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 of the truncated distribution normal mean = parameter0 normal variance = parameters1 lower limit = parameters2 upper limit = parameters3
Sets the parameters of the truncated distribution normal mean = parameter0 normal variance = parameters1 lower limit = parameters2 upper limit = parameters3
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.