Description Usage Arguments Value Author(s) References See Also Examples
p_laplace
computes the probability density function
of a random variable that has a Laplace distribution with parameters μ and
σ.
p_gaussian
computes the probability density function
of a random variable that has a Gaussian distribution with parameters μ and
σ^2.
p_beta
computes the probability density function
of a random variable that has a Beta distribution with parameters α and
β.
p_weibull
computes the probability density function
of a random variable that has a Weibull distribution with parameters κ
and λ.
p_moge
computes the probability density function
of a random variable that has a MOGE distribution with parameters λ,α
and θ.
1 2 3 4 5 6 7 8 9 |
x |
vector of points which values we want to compute. |
mu |
location or mean parameter of the Laplace or Gaussian distribution, respectively. |
sigma |
scale parameter of the Laplace distribution. |
sigma_cuad |
variance parameter of the Gaussian distribution. |
alpha |
shape1 parameter of the Beta distribution or second parameter of the MOGE distribution. |
beta |
shape2 parameter of the Beta distribution. |
k |
shape parameter of the Weibull distribution. |
lambda |
scale parameter of the Weibull distribution or first parameter of the MOGE distribution. |
theta |
third parameter of the MOGE distribution. |
Returns a numeric
object corresponding to the value
of the probability density function for the given x and distribution parameters.
Jesus Prada, jesus.prada@estudiante.uam.es
Link to the scientific paper
Prada, Jesus, and Jose Ramon Dorronsoro. "SVRs and Uncertainty Estimates in Wind Energy Prediction." Advances in Computational Intelligence. Springer International Publishing, 2015. 564-577,
with theoretical background for this package is provided below.
http://link.springer.com/chapter/10.1007/978-3-319-19222-2_47
dlaplace
dnorm
dbeta
dweibull
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