Gallery of examples¶

Assisted specification with Biogeme¶

Examples discussed in Bierlaire and Ortelli (2023) Assisted Specification with Biogeme 3.2.12

Combination of many specifications

Combination of many specifications

Base model

Investigation of several choice models

Investigation of several choice models

Catalog of nonlinear specifications

Catalog of nonlinear specifications

Catalog for alternative specific coefficients

Catalog for alternative specific coefficients

Catalog for segmented parameters

Catalog for segmented parameters

Segmentations and alternative specific specification

Segmentations and alternative specific specification

Combine many specifications: exception is raised

Combine many specifications: exception is raised

Combine many specifications: assisted specification algorithm

Combine many specifications: assisted specification algorithm

One model among many

One model among many

Re-estimation of best models

Re-estimation of best models

Example of a catalog

Example of a catalog

Calculating indicators with Biogeme¶

Examples discussed in Bierlaire (2018) Calculating indicators with PandasBiogeme

Examples of mathematical expressions

Examples of mathematical expressions

Estimation and simulation of a nested logit model

Estimation and simulation of a nested logit model

Simulation of a choice model

Simulation of a choice model

Calculation of market shares

Calculation of market shares

Calculation of revenues

Calculation of revenues

Direct point elasticities

Direct point elasticities

Cross point elasticities

Cross point elasticities

Arc elasticities

Arc elasticities

Calculation of willingness to pay

Calculation of willingness to pay

Specification of a nested logit model

Specification of a nested logit model

Biogeme examples for hybrid choice models¶

You find here an example of choice models with latent variables.

Specification of the choice model

Specification of the choice model

Likelihood function

Likelihood function

Specification of the continuous measurement equations

Specification of the continuous measurement equations

Specification of the discrete measurement equations

Specification of the discrete measurement equations

Data preparation for Optima

Data preparation for Optima

MIMIC (Multiple Indicators Multiple Causes) model

MIMIC (Multiple Indicators Multiple Causes) model

Estimation of the choice model

Estimation of the choice model

Estimation of the hybrid choice model

Estimation of the hybrid choice model

Read of estimate

Read of estimate

Relevant data for the hybrid choice model

Relevant data for the hybrid choice model

Specification of the structural equations

Specification of the structural equations

Examples for the MDCEV model¶

sphx_glr_auto_examples_mdcev_no_outside_good_gamma_specification.py

File gamma_specification.py

sphx_glr_auto_examples_mdcev_no_outside_good_generalized_specification.py

File generalized_specification.py

sphx_glr_auto_examples_mdcev_no_outside_good_non_monotonic_specification.py

File non_monotonic_specification.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_gamma_estimation.py

File gamma_estimation.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_gamma_forecasting.py

File gamma_forecasting.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_generalized_estimation.py

File generalized_estimation.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_generalized_forecasting.py

File generalized_forecasting.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_non_monotonic_estimation.py

File non_monotonic_estimation.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_non_monotonic_forecasting.py

File non_monotonic_forecasting.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_translated_estimation.py

File translated_estimation.py

sphx_glr_auto_examples_mdcev_no_outside_good_plot_translated_forecasting.py

File translated_forecasting.py

sphx_glr_auto_examples_mdcev_no_outside_good_process_data.py

File process_data.py

Specification of the baseline utilities of a MDCEV model.

Specification of the baseline utilities of a MDCEV model.

sphx_glr_auto_examples_mdcev_no_outside_good_translated_specification.py

File translated_specification.py

Monte-Carlo integration with Biogeme¶

Example discussed in Bierlaire (2019) Monte-Carlo integration with Biogeme

Specification of the mixtures of logit

Specification of the mixtures of logit

Simple integral

Simple integral

Various integration methods

Various integration methods

Antithetic draws

Antithetic draws

Antithetic draws explicitly generated

Antithetic draws explicitly generated

Numerical integration

Numerical integration

Monte-Carlo integration

Monte-Carlo integration

Estimation of mixtures of logit

Estimation of mixtures of logit

Mixtures of logit with Monte-Carlo 10_000 draws

Mixtures of logit with Monte-Carlo 10_000 draws

Mixtures of logit with Monte-Carlo 500 draws

Mixtures of logit with Monte-Carlo 500 draws

Mixtures of logit with Monte-Carlo 10_000 antithetic draws

Mixtures of logit with Monte-Carlo 10_000 antithetic draws

Mixtures of logit with Monte-Carlo 500 antithetic draws

Mixtures of logit with Monte-Carlo 500 antithetic draws

Mixtures of logit with Monte-Carlo 10_000 Halton draws

Mixtures of logit with Monte-Carlo 10_000 Halton draws

Mixtures of logit with Monte-Carlo 500 Halton draws

Mixtures of logit with Monte-Carlo 500 Halton draws

Mixtures of logit with Monte-Carlo 10_000 MLHS draws

Mixtures of logit with Monte-Carlo 10_000 MLHS draws

Mixtures of logit with Monte-Carlo 500 MLHS draws

Mixtures of logit with Monte-Carlo 500 MLHS draws

Mixtures of logit with Monte-Carlo 10_000 antithetic MLHS draws

Mixtures of logit with Monte-Carlo 10_000 antithetic MLHS draws

Mixtures of logit with Monte-Carlo 2000 antithetic MLHS draws

Mixtures of logit with Monte-Carlo 2000 antithetic MLHS draws

Data preparation for Swissmetro

Data preparation for Swissmetro

Data preparation for Swissmetro: one observation

Data preparation for Swissmetro: one observation

Programming with Biogeme¶

Examples of the use of various Biogeme objects for programming.

biogeme.biogeme

biogeme.biogeme

biogeme.biogeme_logging

biogeme.biogeme_logging

biogeme.cnl

biogeme.database

biogeme.database

biogeme.distributions

biogeme.distributions

biogeme.draws

biogeme.expressions

biogeme.expressions

biogeme.filenames

biogeme.filenames

biogeme.loglikelihood

biogeme.loglikelihood

biogeme.models

biogeme.nests

biogeme.optimization

biogeme.optimization

biogeme.results_processing

biogeme.results_processing

biogeme.segmentation

biogeme.segmentation

biogeme.tools

biogeme.version

biogeme.version

Sampling of alternatives¶

Examples discussed in Bierlaire and Paschalidis (2023) Estimating MEV models with samples of alternatives

List of alternatives

List of alternatives

Compare parameters

Compare parameters

Logit

Nested logit

Cross-nested logit

Cross-nested logit

Model specification

Model specification

Model specification

Model specification

True parameters

True parameters

Biogeme examples for the Swissmetro data¶

You find here several examples of models that can be estimated and simulated with Biogeme.

Estimation of a logit model

Estimation of a logit model

Logit model

Illustration of additional features of Biogeme

Illustration of additional features of Biogeme

Simulation of a logit model

Simulation of a logit model

Illustration the quick_estimate of Biogeme

Illustration the quick_estimate of Biogeme

WESML

Heteroscedastic specification

Heteroscedastic specification

Out-of-sample validation

Out-of-sample validation

Mixture of logit models

Mixture of logit models

Mixture of logit

Mixture of logit

Mixture of logit models

Mixture of logit models

Simulation of a mixture model

Simulation of a mixture model

Mixture of logit models

Mixture of logit models

Mixture of logit models

Mixture of logit models

sphx_glr_auto_examples_swissmetro_plot_b06unif_mixture_integral.py

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Latent class model

Latent class model

Box-Cox transforms

Box-Cox transforms

Nested logit model

Nested logit model

Nested logit model normalized from bottom

Nested logit model normalized from bottom

Cross-nested logit

Cross-nested logit

Cross-nested logit

Cross-nested logit

Simulation of a cross-nested logit model

Simulation of a cross-nested logit model

Cross-nested logit

Cross-nested logit

Mixture of logit with panel data

Mixture of logit with panel data

Simulation of panel model

Simulation of panel model

Nested logit with corrections for endogeneous sampling

Nested logit with corrections for endogeneous sampling

Discrete mixture with panel data

Discrete mixture with panel data

Discrete mixture with panel data

Discrete mixture with panel data

Discrete mixture with panel data

Discrete mixture with panel data

Mixture with lognormal distribution

Mixture with lognormal distribution

Mixture with lognormal distribution

Mixture with lognormal distribution

Ordinal logit model

Ordinal logit model

Ordinal probit model

Ordinal probit model

Calculation of individual level parameters

Calculation of individual level parameters

Estimation of several models

Estimation of several models

Assisted specification

Assisted specification

Specification of a catalog of models

Specification of a catalog of models

Re-estimate the Pareto optimal models

Re-estimate the Pareto optimal models

Assisted specification

Assisted specification

Specification of a catalog of models

Specification of a catalog of models

Re-estimate the Pareto optimal models

Re-estimate the Pareto optimal models

Binary logit model

Binary logit model

Binary probit model

Binary probit model

Mixture of logit with Halton draws

Mixture of logit with Halton draws

Triangular mixture of logit

Triangular mixture of logit

Triangular mixture with panel data

Triangular mixture with panel data

Data preparation for Swissmetro (binary choice)

Data preparation for Swissmetro (binary choice)

Data preparation for Swissmetro

Data preparation for Swissmetro

Panel data preparation for Swissmetro

Panel data preparation for Swissmetro

Timing function evaluation¶

We perform here the timing on some functions. The results clearly depend on the computer where it is run.

Timing of a logit model

Timing of a logit model

Timing of a cross-nested logit model

Timing of a cross-nested logit model

Timing of a logit model

Timing of a logit model

Comparison of execution times

Comparison of execution times

Data preparation for Swissmetro

Data preparation for Swissmetro

Tool for timing an expression

Tool for timing an expression

Timing of any expression

Timing of any expression

Some simple examples for beginners¶

Estimation of a binary logit model

Estimation of a binary logit model

Configuring Biogeme with parameters

Configuring Biogeme with parameters

Importing model specification

Importing model specification

Estimation results

Estimation results

Using the estimated model

Using the estimated model

Data definition for the simple tutorial

Data definition for the simple tutorial

Model specification for the simple tutorial

Model specification for the simple tutorial

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