Concept
Overdispersion
Publications associées (15)
Fast Bayesian estimation of spatial count data models
Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Mont ...
2021Quantifying the Effects of Contact Tracing, Testing, and Containment Measures in the Presence of Infection Hotspots
Multiple lines of evidence at the individual and population level strongly suggest that infection hotspots, or superspreading events, where a single individual infects many others, play a key role in the transmission dynamics of COVID-19. However, most of ...
2020A new spatial count data model with Bayesian additive regression trees for accident hot spot identification
The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks. Typically, these methods assume ...
2020A Dirichlet process mixture model of discrete choice: Comparisons and a case study on preferences for shared automated vehicles
This paper i) compares parametric and semi-parametric representations of unobserved heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer distributions of willingness to pay for features of shared automated vehicle (SAV ...
2020What Reminds Young People That They Drank More Than Intended on Weekend Nights: An Event-Level Study
Objective:
Young people often drink more alcohol than intended over the course of a night. This study investigates individual and night-specific factors predicting young people’s acknowledgment of having drunk more than intended.
Method:
Using the Yout ...
2018A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning
Aude Billard, Nadia Barbara Figueroa Fernandez
We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) on trajectory data. Physical-consistency of the GMM is ensured by imposing a prior on the component assignments biased by a novel similarity metri ...
2018Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies
We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomi ...
Wiley-Blackwell2016Comparison of Models for Olfactometer Data
Anthony Christopher Davison, Ingrid Ricard
Olfactometer experiments are used to study the responses of arthropods to potential attractants, for purposes such as understanding natural defenses of plants against their herbivores. Such experiments typically lead to multivariate data consisting of smal ...
Springer-Verlag2011Applying the Multivariate Time-Rescaling Theorem to Neural Population Models
Statistical models of neural activity are integral to modern neuroscience. Recently interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on ne ...
Massachusetts Institute of Technology Press2011Systematically heterogeneous covariance in network GEV Models
Mixed logit models can represent heterogeneity across individuals, in both observed and unobserved preferences, but require computationally expensive calculations to compute probabilities. A few methods for including error covariance heterogeneity in a clo ...
2009