Mixtures of logit with Monte-Carlo 500 draws

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

Michel Bierlaire, EPFL Sun Jun 29 2025, 03:47:53

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_500.py')

Example b07estimation_monte_carlo_500.py

R = 500

the_draws = Draws('b_time_rnd', 'NORMAL')
the_biogeme = get_biogeme(the_draws=the_draws, number_of_draws=R)
the_biogeme.model_name = 'b07estimation_monte_carlo_500'
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_500
Nbr of parameters:              5
Sample size:                    10719
Excluded data:                  9
Final log likelihood:           -8571.677
Akaike Information Criterion:   17153.35
Bayesian Information Criterion: 17189.75

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 -0.468894         0.047924       -9.784020    0.000000e+00
1     b_time -1.855878         0.075706      -24.514320    0.000000e+00
2   b_time_s  1.194822         0.087577       13.643032    0.000000e+00
3     b_cost -0.844763         0.057551      -14.678422    0.000000e+00
4    asc_car  0.177860         0.035109        5.065943    4.063836e-07}