Mixtures of logit with Monte-Carlo 10_000 antithetic draws

Estimation of a mixtures of logit models where the integral is approximated using MonteCarlo integration with antithetic draws.

Michel Bierlaire, EPFL Sun Jun 29 2025, 00:37:30

from b07estimation_specification import get_biogeme
from IPython.core.display_functions import display

import biogeme.biogeme_logging as blog
from biogeme.expressions import Draws
from biogeme.results_processing import (
    EstimationResults,
    get_pandas_estimated_parameters,
)

logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b07estimation_monte_carlo_anti.py')

Example b07estimation_monte_carlo_anti.py

R = 10_000

the_draws = Draws('b_time_rnd', 'NORMAL_ANTI')
the_biogeme = get_biogeme(the_draws=the_draws, number_of_draws=R)
the_biogeme.model_name = 'b07estimation_monte_carlo_anti'
results_file = f'saved_results/{the_biogeme.model_name}.yaml'

Biogeme parameters read from biogeme.toml.

try:
    results = EstimationResults.from_yaml_file(filename=results_file)
except FileNotFoundError:
    results = the_biogeme.estimate()

print(results.short_summary())

Results for model b07estimation_monte_carlo_anti
Nbr of parameters:              5
Sample size:                    10719
Excluded data:                  9
Final log likelihood:           -8570.858
Akaike Information Criterion:   17151.72
Bayesian Information Criterion: 17188.11

Get the results in a pandas table

pandas_results = get_pandas_estimated_parameters(
    estimation_results=results,
)
display(pandas_results)

{'Estimated parameters':         Name     Value  Robust std err.  Robust t-stat.  Robust p-value
0  asc_train -0.467162         0.047986       -9.735444    0.000000e+00
1     b_time -1.864136         0.075888      -24.564153    0.000000e+00
2   b_time_s  1.209920         0.087471       13.832316    0.000000e+00
3     b_cost -0.845646         0.057676      -14.662058    0.000000e+00
4    asc_car  0.180352         0.035153        5.130487    2.889934e-07}