Biogeme examples for Bayesian inference with the Swissmetro dataΒΆ

You find here several examples of models that illustrate how to specify models to be estimated with Biogeme using Bayesian inference. To the extent possible, we have used the same examples illustrating the maximum likelihood estimation. The names of the files should correspond too.

19. Calculation of individual level parameters

19. Calculation of individual level parameters

18a. Ordinal logit model

18a. Ordinal logit model

18. Ordinal probit model

18. Ordinal probit model

4. Out-of-sample validation

4. Out-of-sample validation

23a. Binary logit model

23a. Binary logit model

23b. Binary probit model

23b. Binary probit model

1a. Estimation of a logit model (Bayesian)

1a. Estimation of a logit model (Bayesian)

6. Mixture of logit models: uniform distribution

6. Mixture of logit models: uniform distribution

1c. Simulation of a logit model (traditional and Bayesian)

1c. Simulation of a logit model (traditional and Bayesian)

3. Moneymetric and heteroscedastic specification

3. Moneymetric and heteroscedastic specification

17. Mixture with lognormal distribution

17. Mixture with lognormal distribution

8. Box-Cox transforms

8. Box-Cox transforms

5. Mixture of logit models: normal distribution

5. Mixture of logit models: normal distribution

10. Nested logit model normalized from bottom

10. Nested logit model normalized from bottom

7. Latent class model

7. Latent class model

9. Nested logit model

9. Nested logit model

2. Logit and sample with weights (Bayesian)

2. Logit and sample with weights (Bayesian)

25. Triangular mixture of logit

25. Triangular mixture of logit

1b. Estimation of a logit model with custom priors (Bayesian)

1b. Estimation of a logit model with custom priors (Bayesian)

12. Mixture of logit with panel data

12. Mixture of logit with panel data

26. Triangular mixture with panel data

26. Triangular mixture with panel data

11. Cross-nested logit

11. Cross-nested logit

15. Discrete mixture with panel data

15. Discrete mixture with panel data

16. Latent class model with panel data

16. Latent class model with panel data