10 citations · 24 across the 11 of their papers we have counts for
11 papers
Hierarchical Causal Structure Learning
Sjoerd Hermes, Joost van Heerwaarden, Fred van Eeuwijk +1
Traditional statistical approaches primarily model associations between variables, however many scientific and practical questions require causal methods instead. These methods typ…
Multi-Attribute Preferences: A Transfer Learning Approach
Sjoerd Hermes, Joost van Heerwaarden, Pariya Behrouzi
We introduce a transfer-learning method based on the Bradley--Terry model for multi-attribute pairwise-comparison data. The aim is to estimate the log-worth parameters of one prima…
Joint Learning from Heterogeneous Rank Data
Sjoerd Hermes, Joost van Heerwaarden, Pariya Behrouzi
The statistical modelling of ranking data has a long history and encompasses various perspectives on how observed rankings arise. One of the most common models, the Plackett-Luce m…
Multi-omics network reconstruction with collaborative graphical lasso
Alessio Albanese, Wouter Kohlen, Pariya Behrouzi
Motivation: In recent years, the availability of multi-omics data has increased substantially. Multi-omics data integration methods mainly aim to leverage different molecular layer…
A Spatial Autoregressive Graphical Model with Applications in Intercropping
Sjoerd Hermes, Joost van Heerwaarden, Pariya Behrouzi
Within the statistical literature, a significant gap exists in methods capable of modeling asymmetric multivariate spatial effects that elucidate the relationships underlying compl…
Copula Graphical Models for Heterogeneous Mixed Data
Sjoerd Hermes, Joost van Heerwaarden, Pariya Behrouzi
This article proposes a graphical model that handles mixed-type, multi-group data. The motivation for such a model originates from real-world observational data, which often contai…
Bayesian Structural Learning with Parametric Marginals for Count Data: An Application to Microbiota Systems
Veronica Vinciotti, Pariya Behrouzi, Reza Mohammadi
High dimensional and heterogeneous count data are collected in various applied fields. In this paper, we look closely at high-resolution sequencing data on the microbiome, which ha…
Dynamic Chain Graph Models for Ordinal Time Series Data
Pariya Behrouzi, Fentaw Abegaz, Ernst C. Wit
This paper introduces sparse dynamic chain graph models for network inference in high dimensional non-Gaussian time series data. The proposed method parametrized by a precision mat…
netgwas: An R Package for Network-Based Genome-Wide Association Studies
Pariya Behrouzi, Danny Arends, Ernst C. Wit
Graphical models are a powerful tool in modelling and analysing complex biological associations in high-dimensional data. The R-package netgwas implements the recent methodological…
De novo construction of polyploid linkage maps using discrete graphical models
Pariya Behrouzi, Ernst C. Wit
Linkage maps are used to identify the location of genes responsible for traits and diseases. New sequencing techniques have created opportunities to substantially increase the dens…
Detecting Epistatic Selection with Partially Observed Genotype Data Using Copula Graphical Models
P. Behrouzi, E. C. Wit
Recombinant Inbred Lines derived from divergent parental lines can display extensive segregation distortion and long-range linkage disequilibrium (LD) between distant loci. These g…