pymultinest_demo.py

#!/usr/bin/env python
from __future__ import absolute_import, unicode_literals, print_function
import numpy
from numpy import pi, cos
from pymultinest.solve import solve
import os

try:
    os.mkdir('chains')
except OSError:
    pass


# probability function, taken from the eggbox problem.

def myprior(cube):
    return cube * 10 * pi


def myloglike(cube):
    chi = (cos(cube / 2.)).prod()
    return (2. + chi) ** 5


# number of dimensions our problem has
parameters = ["x", "y"]
n_params = len(parameters)
# name of the output files
prefix = "chains/3-"

# run MultiNest
result = solve(LogLikelihood=myloglike, Prior=myprior,
               n_dims=n_params, outputfiles_basename=prefix, verbose=True)

print()
print('evidence: %(logZ).1f +- %(logZerr).1f' % result)
print()
print('parameter values:')
for name, col in zip(parameters, result['samples'].transpose()):
    print('%15s : %.3f +- %.3f' % (name, col.mean(), col.std()))

# make marginal plots by running:
# $ python multinest_marginals.py chains/3-
# For that, we need to store the parameter names:
import json

with open('%sparams.json' % prefix, 'w') as f:
    json.dump(parameters, f, indent=2)