Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Bierlaire, Michel"'
The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the considerable number of parameters involved in these models. This complexity h
Externí odkaz:
http://arxiv.org/abs/2402.06763
Conditionality has become a core component for Generative Adversarial Networks (GANs) for generating synthetic images. GANs are usually using latent conditionality to control the generation process. However, tabular data only contains manifest variab
Externí odkaz:
http://arxiv.org/abs/2210.02404
Synthetic data can be used in various applications, such as correcting bias datasets or replacing scarce original data for simulation purposes. Generative Adversarial Networks (GANs) are considered state-of-the-art for developing generative models. H
Externí odkaz:
http://arxiv.org/abs/2203.03489
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the obs
Externí odkaz:
http://arxiv.org/abs/1906.03855
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel. To address this
Externí odkaz:
http://arxiv.org/abs/1904.07688
Publikováno v:
Transportation Research Part B: Methodological, Volume 131, January 2020, Pages 124-142
Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is s
Externí odkaz:
http://arxiv.org/abs/1904.03647