Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

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Multi-frequency and Doppler spectral radars are valuable tools for retrieving information on the microphysics of precipitation, particularly snowfall. However, estimating microphysical properties from radar measurements is an ill-posed problem, i.e., diffe ...

Derived from the Karush–Kuhn–Tucker (KKT) optimality conditions of utility maximization, the multiple discrete–continuous extreme value (MDCEV) models are extensively used in the field of time use. This is because they are grounded in a framework that maxi ...

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, t ...

Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that the covariance is sample dependent and is often unknown. Consequently, recen ...

Computer vision has made remarkable strides in recent decades, becoming a cornerstone of modern technology with applications ranging from autonomous driving to medical imaging. However, despite these advancements, several core challenges remain, especially ...

Outliers in discrete choice response data may result from misclassification and misreporting of the response variable and from choice behaviour that is inconsistent with modelling assumptions (e.g. random utility maximisation). In the presence of outliers, ...

The objective of this study is to address the difficulty of simplifying the geometric model in which a differential problem is formulated, also called defeaturing, while simultaneously ensuring that the accuracy of the solution is maintained under control. ...

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are consi ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

This paper explores data assimilation methods in the context of Bayesian Calibration for fuel performance models. It compares deterministic and stochastic approaches, concentrating on the Generalized Linear Least Squares (GLLS) method and stochastic techni ...

Local modifications of a computational domain are often performed in order to simplify the meshing process and to reduce computational costs and memory requirements. However, removing geometrical features of a domain often introduces a non-negligible error ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

This paper presents the experimental validation of a linear recursive state estimation (SE) process for hybrid AC/DC microgrids proposed in the authors' previous work. The SE uses a unified and linear measurement model that relies on the use of synchronize ...

Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...

In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright–Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and is based on exact simulation techniques that are of s ...

The p-Laplacian problem -del & sdot; ((mu + |del u|(p-2))del u) = f is considered, where mu is a given positive number. An anisotropic a posteriori residual-based error estimator is presented. The error estimator is shown to be equivalent, up to higher ord ...

We introduce Neural Network (NN for short) approximation architectures for the numerical solution of Boundary Integral Equations (BIEs for short). We exemplify the proposed NN approach for the boundary reduction of the potential problem in two spatial dime ...

The manuscript describes the development of a spatial Agent-based Simulation to model the effect of public policies on private houseowner’s decisions concerning their heating system. The methodology utilized comprises of an empirical survey to determine th ...

The use of discrete choice models (DCMs) is a regular approach to investigating migration aspirations concerning destination choices. However, given the complex substitution patterns between destinations, more advanced model specifications than the multino ...

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

Chattez avec Graph Search

What can radars tell us about snowfall microphysics? Insights from a Markov chain Monte-Carlo approach

A new flexible multiple discrete–continuous extreme value (MDCEV) choice model: exploring non-monotonic preference in time use decisions

Improved bounds and inference on optimal regimes

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

Improving Human Pose & Shape Estimation with Explicit and Implicit Priors

Robust discrete choice models with t-distributed kernel errors

A Shape Derivative Approach to Domain Simplification

An equilibrated flux a posteriori error estimator for defeaturing problems

Spectral Estimators for High-Dimensional Matrix Inference

Stochastic adaptive resampling for the estimation of discrete choice models

Comparison of Bayesian Model Calibration Techniques for Future Application to Fuel Performance Behavior Models

Analysis-aware defeaturing of complex geometries with Neumann features

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Experimental Validation of a Unified and Linear State Estimation Method for Hybrid AC/DC Microgrids

OASIS: An integrated optimisation framework for activity scheduling

Unbiased Likelihood Estimation of Wright–Fisher Diffusion Processes

Anisotropic Adaptive Finite Elements for a p-Laplacian Problem

ReLU Neural Network Galerkin BEM

Spatial Agent-Based Modelling and Simulation to Evaluate on Public Policies for Energy Transition

Sampling of alternatives in migration aspiration models