Skip to content
Log in
Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

Abdolnasser Sadeghkhani

h-index 3 View on Semantic Scholar

We have 4 of 35 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Oct 2026

Intrinsic predictive density estimation on the sphere under additional information

We study predictive density estimation on the sphere under additional information. Specifically, we consider geodesic normal models on $$\mathbb {S}^p$$ in which the parameter pair $$(\mu _1,\mu _2)$$ is subject to an intrinsic restriction expressed through the logarithm map and geodesic distance. This...

Abdolnasser Sadeghkhani · 0 citations
Open access Oct 2026

Robust Bayesian exceedance screening for count data

Rare-event screening in count data often concerns latent upper-tail risk rather than large observed counts alone. We develop a robust Bayesian procedure whose parameter-conditional tail functional is θi(ϑ)=Prϑ(Yirep>τi∣xi)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \use...

Abdolnasser Sadeghkhani · 0 citations
Preprint Jul 2026

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks

A Laplace-approximated Bayesian scoring function for the non-conjugate Normal--Gamma prior is developed and, through the DAG-probit extension, predicts malignancy from nuclear morphometry with a cross-validated ROC-AUC of $0.94$ using a sparse, interpretable set of direct predictors.

Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani · 0 citations
Preprint Jul 2026

Bayesian DAG Structure Learning with Simultaneous Shrinkage Covariance Estimation under Scale-Mixture Error Distributions in the Proportional High-Dimensional Regime

We propose a unified Bayesian framework namely robust DAG-Cholesky horseshoe (R-DACH) for joint directed acyclic graph (DAG) structure learning and precision matrix estimation in the high-dimensional proportional asymptotic regime $p/n \to c \in (0,\infty)$, under the scale mixture of normal errors. The construction pl...

Samaneh Nazari, Mohammad Arashi, Abdolnasser Sadeghkhani · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.