Class Aoi_lstKt
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- All Implemented Interfaces:
public final class Aoi_lstKt
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Method Summary
Modifier and Type Method Description final static LstFunctionaoi_lst_exp(Double mu)Laplace-Stieltjes transform for exponential distribution. final static LstFunctionaoi_lst_det(Double d)Laplace-Stieltjes transform for deterministic (constant) distribution. final static LstFunctionaoi_lst_erlang(Integer k, Double mu)Laplace-Stieltjes transform for Erlang distribution. final static LstFunctionaoi_lst_ph(Matrix alpha, Matrix T)Laplace-Stieltjes transform for phase-type distribution. -
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Method Detail
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aoi_lst_exp
final static LstFunction aoi_lst_exp(Double mu)
Laplace-Stieltjes transform for exponential distribution.
Returns an LstFunction for the LST of an exponential distribution with rate mu: H*(s) = mu / (mu + s)
- Parameters:
mu- Rate parameter (mean = 1/mu), must be positive- Returns:
LstFunction computing mu / (mu + s)
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aoi_lst_det
final static LstFunction aoi_lst_det(Double d)
Laplace-Stieltjes transform for deterministic (constant) distribution.
Returns an LstFunction for the LST of a deterministic distribution with constant value d: H*(s) = exp(-s * d)
- Parameters:
d- Constant value, must be positive- Returns:
LstFunction computing exp(-s * d)
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aoi_lst_erlang
final static LstFunction aoi_lst_erlang(Integer k, Double mu)
Laplace-Stieltjes transform for Erlang distribution.
Returns an LstFunction for the LST of an Erlang-k distribution with rate parameter mu: H*(s) = (mu / (mu + s))^k
- Parameters:
k- Shape parameter (number of phases), must be a positive integermu- Rate parameter per phase (mean = k/mu), must be positive- Returns:
LstFunction computing (mu / (mu + s))^k
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aoi_lst_ph
final static LstFunction aoi_lst_ph(Matrix alpha, Matrix T)
Laplace-Stieltjes transform for phase-type distribution.
Returns an LstFunction for the LST of a phase-type (PH) distribution with initial probability vector alpha and sub-generator matrix T.
H*(s) = alpha * (s*I - T)^{-1} * (-T * e)
where e = ones(n,1) is the vector of ones.
- Parameters:
alpha- Initial probability row vector (1 x n)T- Sub-generator matrix (n x n)- Returns:
LstFunction computing alpha * inv(s*I - T) * t
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