properGeneralizedInverseGaussianFixedLambdaEstimate

import std.range;
import std.random;
import atmosphere.random;
import atmosphere.likelihood;
auto length = 1000;
auto lambda = -2.0, eta = 1.4, omega = 2.3;
auto rng = Random(1234);
auto sample = ProperGeneralizedInverseGaussianSRNG!double(rng, lambda, eta, omega).take(length).array;
auto weights = iota(1.0, length + 1.0).array;
auto params = properGeneralizedInverseGaussianFixedLambdaEstimate!double(lambda, sample, weights);
auto lh0 = properGeneralizedInverseGaussianLikelihood(lambda, eta, omega, sample, weights);
auto lh1 = properGeneralizedInverseGaussianLikelihood(lambda, params.eta, params.omega, sample, weights);
assert(lh0 <= lh1);

import std.range;
import std.random;
import atmosphere.random;
import atmosphere.likelihood;
auto length = 1000;
auto lambda = -2.0, eta = 1.4, omega = 2.3;
auto rng = Random(1234);
auto sample = ProperGeneralizedInverseGaussianSRNG!double(rng, lambda, eta, omega).take(length).array;
auto params = properGeneralizedInverseGaussianFixedLambdaEstimate!double(lambda, sample);
auto lh0 = properGeneralizedInverseGaussianLikelihood(lambda, eta, omega, sample);
auto lh1 = properGeneralizedInverseGaussianLikelihood(lambda, params.eta, params.omega, sample);
assert(lh0 <= lh1);