Reza Mohammadi

University of Amsterdam | UVA · Amsterdam Business School

Decoding complex relationships among large numbers of variables with relatively few observations is one of the crucial issues in science. One approach to this problem is Gaussian graphical modeling, which describes conditional independence of variables through the presence or absence of edges in the underlying graph. In this paper, we introduce a n...

The package ’BDgraph’ is a statistical tool for model selection in undirected Gaussian graphical models. Our Bayesian methodology is based on birth-death MCMC algorithm. The main function is ’bdgraph’ which is a birth-death MCMC algorithm.

In this article, we exploit the Bayesian inference and prediction for an M/G/1 queuing model with optional second re-service. In this model, a service unit attends customers arriving following a Poisson process and demanding service according to a general distribution and some of customers need to re-service with probability “p”. First, we introduc...

The paper proposes Bayesian framework in an M/G/1 queuing system with optional second service. The semi-parametric model based on a finite mixture of Gamma distributions is considered to approximate both the general service and re-service times densities in this queuing system. A Bayesian procedure based on birth-death MCMC methodology is proposed...

Dupuytren disease is a fibroproliferative disorder with unknown etiology that often progresses and eventually can cause permanent contractures of the affected fingers. Most of the researches on severity of the disease and the phenotype of this disease are observational studies without concrete statistical analyses. There is a lack of multivariate a...

Introduction This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable ris...

Network psychometrics is a new direction in psychological research that conceptualizes psychological constructs as systems of interacting variables. In network analysis, variables are represented as nodes, and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for prop...

Gaussian graphical models are graphs that represent the conditional relationships among multivariate normal variables. The process of uncovering the structure of these graphs is known as structure learning. Despite the fact that Bayesian methods in structure learning offer intuitive and well-founded ways to measure model uncertainty and integrate p...

Gaussian graphical models depict the conditional dependencies between variables within a multivariate normal distribution in a graphical format. The identification of these graph structures is an area known as structure learning. However, when utilizing Bayesian methodologies in structure learning, computational complexities can arise, especially w...

Background The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives The present study was aimed at identifying the predicting factors for delayed BC...

Network psychometrics is a new direction in psychological research that conceptualizes multivariate data as interacting systems. Variables are represented as nodes and their interactions yield (partial) associations. Current estimation methods mostly use a frequentist approach, which does not allow for proper uncertainty quantification of the model...

The operating rooms within the surgical unit take center stage in a hospital. The fact that, in practice, actual durations of surgery do not coincide with their allotted times yields extra costs; for example, earliness results in unutilized operating room time, and lateness incurs extra waiting for patients. Various machine learning methods are emp...

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburden...

The COVID-19 pandemic shows to have a huge impact on people's health and countries' infrastructures around the globe. Iran was one of the first countries that experienced the vast prevalence of the coronavirus outbreak. The Iranian authorities applied various non-pharmaceutical interventions to eradicate the epidemic in different periods. This stud...

Metagenomics combined with high-resolution sequencing techniques have enabled researchers to study the genomes of entire microbial communities. Unraveling interactions between these communities is of vital importance to understand how microbes influence human health and disease. However, learning these interactions from microbiome data is challengi...

Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient's b...

Bayesian structure learning in Gaussian graphical models is often done by search algorithms over the graph space.The conjugate prior for the precision matrix satisfying graphical constraints is the well-known G-Wishart.With this prior, the transition probabilities in the search algorithms necessitate evaluating the ratios of the prior normalizing c...

Recently, Xiong et al. (2019) introduced an alternative measure of uncertainty known as the fractional cumulative residual entropy (FCRE). In this paper, first, we study some general properties of FCRE and its dynamic version. We also consider a version of fractional cumulative paired entropy for a random lifetime. Then we apply the FCRE measure fo...

The COVID-19 pandemic has had a huge impact on people's health, and countries' infrastructures around the globe. Iran was one of the first countries that experienced the vast prevalence of the coronavirus outbreak. Iranian government applied various nonpharmaceutical interventions to eradicate the epidemic in different periods. To evaluate the effe...

Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using various brain imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Currently, the most approaches to analyze statistical associations between regions and imaging modalities rely on...

Recently, Tahmasebi and Eskandarzadeh introduced a new extended cumulative entropy (ECE). In this paper, we present results on shift-dependent measure of ECE and its dynamic past version. These results contain stochastic order, upper and lower bounds, the symmetry property and some relationships with other reliability functions. We also discuss som...

Graphical models provide powerful tools to uncover complicated patterns in multivariate data and are commonly used in Bayesian statistics and machine learning. In this paper, we introduce the R package BDgraph which performs Bayesian structure learning for general undirected graphical models (decomposable and non-decomposable) with continuous, disc...

Decision trees are flexible models that are well suited for many statistical regression problems. In a Bayesian framework for regression trees, Markov Chain Monte Carlo (MCMC) search algorithms are required to generate samples of tree models according to their posterior probabilities. The critical component of such an MCMC algorithm is to construct...

Alzheimer’s disease is characterized by a cascade of pathological processes that can be assessed in vivo using different neuroimaging methods. Recent research suggests a systematic sequence of pathogenic events on a global biomarker level, but little is known about the associations and dependencies of distinct lesion patterns on a regional level. M...

Several neuroimaging markers have been established for the early diagnosis of Alzheimer’s disease, among them amyloid-β deposition, glucose metabolism, and grey matter volume. Up to now, these imaging modalities were mostly analyzed separately from each other, and little is known about the regional interrelation and dependency of these markers. Gau...

I would first like to congratulate Dr Wade and Professor Ghahramani for their excellent exposition of the Bayesian nonparametric cluster analysis by developing point estimates and credible sets to summarize the posterior of the clustering structure. Their method is based on a greedy search algorithm to locate the optimal partition based on Hasse di...

Several neuroimaging markers have been established for the early diagnosis of Alzheimer's disease, among them amyloid-beta deposition, glucose metabolism, and gray matter volume. Up to now, these imaging modalities were mostly analyzed separately from each other, and little is known about the regional interrelation and dependency of these markers....

Description Provides statistical tools for Bayesian estimation for finite mixture of distributions , mainly mixture of Gamma, Normal and t-distributions. The package is implemented the recent improvements in Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013)

Methods for selecting loglinear models were among Steve Fienberg's research interests since the start of his long and fruitful career. After we dwell upon the string of papers focusing on loglinear models that can be partly attributed to Steve's contributions and influential ideas, we develop a new algorithm for selecting graphical loglinear models...

The ratio of normalizing constants for the G-Wishart distribution, for two graphs differing by an edge e, has long been a bottleneck in the search for efficient model selection in the class of graphical Gaussian models. We give an accurate approximation to this ratio under two assumptions: first, we assume that the scale of the prior is the identit...

The R package bmixture provides statistical tools for Bayesian estimation for the mixture of distributions. The package implemented the improvements in the Bayesian literature, including Mohammadi et al. (2013) and Mohammadi and Salehi-Rad (2012). Besides, the package contains several functions for simulation and visualization, as well as a real da...

Pratola (2016) introduces a novel proposal mechanism for the Metropolis- Hastings step of a Markov chain Monte Carlo (MCMC) sampler that allows efficient traversal of the space of latent stochastic partitions defined by binary regression trees. Here we discuss two considerations: the first is the use of the new proposal mechanism within a populatio...

In this thesis, we address several problems related to modelling complex systems. The difficulty of modelling complex systems lies partly in their topology and how they form rather complex networks. From this perspective, our interest in networks (graphs) is part of a broader current of research on complex systems. Graphical models provide powerful...

We introduce an R package BDgraph which performs Bayesian structure learning in high-dimensional graphical models with either continuous or discrete variables. This package efficiently performs recent improvements in the Bayesian literature. The core of the BDgraph package consists of two main MCMC sampling algorithms efficiently implemented in C++...

Decoding complex relationships among large numbers of variables with relatively small data sets is one of the crucial issues in science. One approach to those problems is Gaussian graphical modeling, which describes conditional independence of variables through the presence or absence of edges in the underlying graph. In this paper, we introduce a...