94 citations · 114 across the 5 of their papers we have counts for
6 papers
Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures
Konstantinos Perrakis, Dimitris Karlis, Mario Cools +1
In this paper we present Poisson mixture approaches for origin-destination (OD) modeling in transportation analysis. We introduce covariate-based models which incorporate different…
Regularized joint mixture models
Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger +1
Regularized regression models are well studied and, under appropriate conditions, offer fast and statistically interpretable results. However, large data in many applications are h…
Scalable Bayesian regression in high dimensions with multiple data sources
Konstantinos Perrakis, Sach Mukherjee, the Alzheimers Disease Neuroimaging Initiative
Applications of high-dimensional regression often involve multiple sources or types of covariates. We propose methodology for this setting, emphasizing the "wide data" regime with…
Variations of Power-Expected-Posterior Priors in Normal Regression Models
Dimitris Fouskakis, Ioannis Ntzoufras, Konstantinos Perrakis
The power-expected-posterior (PEP) prior is an objective prior for Gaussian linear models, which leads to consistent model selection inference, under the M-closed scenario, and ten…
Power-Expected-Posterior Priors for Generalized Linear Models
Dimitris Fouskakis, Ioannis Ntzoufras, Konstantinos Perrakis
The power-expected-posterior (PEP) prior provides an objective, automatic, consistent and parsimonious model selection procedure. At the same time it resolves the conceptual and co…
On the use of marginal posteriors in marginal likelihood estimation via importance-sampling
K. Perrakis, I. Ntzoufras, E. G. Tsionas
We investigate the efficiency of a marginal likelihood estimator where the product of the marginal posterior distributions is used as an importance-sampling function. The approach…