Package jline.api.pfqn.nc

Class Pfqn_mci

java.lang.Object
jline.api.pfqn.nc.Pfqn_mci
public final class Pfqn_mci extends Object

  • Method Details

    • pfqn_mci

      public static Ret.pfqnNc pfqn_mci(Matrix D, Matrix N, Matrix Z, int I, String variant)
      Monte Carlo integration for the normalizing constant.

      Variants: mci, imci (improved), amci, lhsmci and rm (repairman). amci and lhsmci use the imci tilt and differ only in how the uniforms are drawn.

      amci draws ANTITHETIC pairs (u, 1-u). This does NOT reliably reduce variance here: the tilted integrand is not monotone in the exponential draws (the tilt term -(1-gamma)V decreases while the N log(VD+Z) term increases), so the pair correlation is not systematically negative; measured variance ratios against imci range from 0.54 to 1.6 across models. It is kept because it is the Ross-Wang construction, not because it is the better default. lhsmci stratifies each coordinate by Latin hypercube sampling, which IS reliably variance-reducing on the same models (ratios 0.0 to 0.48, exact quadrature in the limit of one station) at O(I log I) extra cost.

      Parameters:
      D - service demand matrix (M x R)
      N - population vector (1 x R)
      Z - think time vector (1 x R)
      I - number of samples
      variant - sampling variant
      Returns:
      the normalizing constant and its logarithm

    • pfqn_mci

      public static Ret.pfqnNc pfqn_mci(Matrix D, Matrix N, Matrix Z, int I, String variant, long seed)
      Monte Carlo integration for the normalizing constant from a fixed seed, so that a fixed seed reproduces the estimate exactly (the SolverNC path passes options.seed).
      Parameters:
      D - service demand matrix (M x R)
      N - population vector (1 x R)
      Z - think time vector (1 x R)
      I - number of samples
      variant - sampling variant
      seed - seed of the generator the draws are taken from
      Returns:
      the normalizing constant and its logarithm

    • pfqn_mci

      public static Ret.pfqnNc pfqn_mci(Matrix D, Matrix N, Matrix Z, int I, String variant, Random rand)
      Monte Carlo integration for the normalizing constant drawing from the caller's generator, which the call advances.
      Parameters:
      D - service demand matrix (M x R)
      N - population vector (1 x R)
      Z - think time vector (1 x R)
      I - number of samples
      variant - sampling variant
      rand - generator the uniforms are drawn from
      Returns:
      the normalizing constant and its logarithm