Ankur Ankan

Joined

February 2022

Names

Ankur Ankan (Preferred)

Emails

****@ru.nl (Confirmed)

Personal Links

Google Scholar

DBLP

Career & Education History

Postdoc

Institute for Computing and Information Sciences, Radboud University Nijmegen, Radboud University (cs.ru.nl)

2024 – 2025

Advisors, Relations & Conflicts

No relations added

Expertise

No areas of expertise listed

Publications

Expert-In-The-Loop Causal Discovery: Iterative Model Refinement Using Expert Knowledge
Ankur Ankan, Johannes Textor
- UAI 2025 Oral
- Readers: Everyone
Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models
Ankur Ankan, Inge Wortel, Johannes Textor, Kenneth Bollen
- International Conference on Artificial Intelligence and Statistics (AISTATS)
- Readers: Everyone
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data
Ankur Ankan, Johannes Textor
- Published: 01 Jan 2023, Last Modified: 08 Feb 2024
- AAAI 2023
- Readers: Everyone
pgmpy: A Python Toolkit for Bayesian Networks
Ankur Ankan, Johannes Textor
- Published: 01 Jan 2023, Last Modified: 08 Feb 2024
- CoRR 2023
- Readers: Everyone
Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation Models
Ankur Ankan, Inge M. N. Wortel, Kenneth Bollen, Johannes Textor
- Published: 01 Jan 2023, Last Modified: 08 Feb 2024
- AISTATS 2023
- Readers: Everyone
Testing Graphical Causal Models Using the R Package “dagitty”
Ankur Ankan, Inge Wortel, Johannes Textor
- 03 Mar 2022
- OpenReview Archive Direct Upload
- Readers: Everyone
A Simple Unified Approach to Testing High-Dimensional Conditional Independences for Categorical and Ordinal Data
Ankur Ankan, Johannes Textor
- Published: 01 Jan 2022, Last Modified: 08 Feb 2024
- CoRR 2022
- Readers: Everyone
pgmpy: Probabilistic Graphical Models using Python
Ankur Ankan, Abinash Panda
- Published: 01 Jan 2015, Last Modified: 21 Feb 2024
- SciPy 2015
- Readers: Everyone