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Gregory Ashton authored- Add a README - Move non-dynesty examples into separate sub-directory to avoid repeatition - Standardize variable names - Remove the multidimensional gaussian: the 15d example is better and more complete - Remove the start_mcmc_chains_near.. example: this is demonstrating a rarely used feature of a rarely used sampler, better to put this into the docs if needed.
Gregory Ashton authored- Add a README - Move non-dynesty examples into separate sub-directory to avoid repeatition - Standardize variable names - Remove the multidimensional gaussian: the 15d example is better and more complete - Remove the start_mcmc_chains_near.. example: this is demonstrating a rarely used feature of a rarely used sampler, better to put this into the docs if needed.
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dirichlet.py 1.05 KiB
import numpy as np
import pandas as pd
from bilby.core.likelihood import Multinomial
from bilby.core.prior import DirichletPriorDict
from bilby.core.sampler import run_sampler
n_dim = 3
label = "dirichlet_"
priors = DirichletPriorDict(n_dim=n_dim, label=label)
injection_parameters = dict(
dirichlet_0=1 / 3,
dirichlet_1=1 / 3,
dirichlet_2=1 / 3,
)
data = [injection_parameters[label + str(ii)] * 1000 for ii in range(n_dim)]
likelihood = Multinomial(data=data, n_dimensions=n_dim, label=label)
result = run_sampler(
likelihood=likelihood, priors=priors, nlive=100,
label="multinomial", injection_parameters=injection_parameters
)
result.posterior[label + str(n_dim - 1)] = 1 - np.sum([result.posterior[key] for key in priors], axis=0)
result.plot_corner(parameters=injection_parameters)
samples = priors.sample(10000)
samples[label + str(n_dim - 1)] = 1 - np.sum([samples[key] for key in samples], axis=0)
result.posterior = pd.DataFrame(samples)
result.plot_corner(parameters=[key for key in samples], filename="outdir/dirichlet_prior_corner.png")