Writing

MaRDI publications

T. Boege, R. Fritze, Ch. Görgen, J. Hanselman, D. Iglezakis, L. Kastner, T. Koprucki, T. Krause, C. Lehrenfeld, S. Polla, M. Reidelbach, C. Riedel, J. Saak, B. Schembera, K. Tabelow, M. Weber (2022). Research-data management planning in the German mathematical community. Accepted for publication by the European Mathematical Society Magazine. Preprint available on arxiv:2211.12071 [math.HO]. 

C. Fevola and Ch. Görgen (2022). The mathematical research-data repository MathRepo. Accepted by the Computer Algebra Rundbrief, Nummer 70. Preprint available on arxiv:2202.04022 [math.HO].

P. Benner, M. Burger, D. Göddeke, Ch. Görgen, C. Himpe, J. Heiland, T. Koprucki, M. Ohlberger, S. Rave, M. Reidelbach, J. Saak, A. Schöbel, K. Tabelow, and M. Weber (2022). Die mathematische Forschungsdateninitiative in der NFDI: MaRDI (Mathematical Research Data Initiative). GAMM Rundbrief 1/2022, pp.40–43.

Ch. Görgen and R. Sinn (2022). Mathematik in der Nationalen Forschungsdateninfrastruktur. Mitteilungen der Deutschen Mathematiker-Vereinigung, Volume 29 (3), pp. 122–123.

MaRDI outreach

For the MaRDI Newsletter, we conducted a series of interviews with mathematicians and research-data experts on the FAIR principles: you can listen to myself talk with Cedric Villani about findability,with Johan Commelin about accessibility, with Ulrike Meier Yang about interoperability, and with Elisabeth Bergherr about reusability. Subscribe!

Mathematics

I used to do research in graphical models and Algebraic Statistics. In particular, I am interested in the interplay of notions from algebraic geometry with questions arising in statistical inference, model validation, causal discovery, and machine learning. 

In 2017 I wrote a short text about 'What is algebraic statistics?' for a local Algebraic Statistics Day.

Maths publications

Ch. Görgen, A. Maraj, and L. Nicklasson (2022). Staged tree models with toric structure. Journal of Symbolic Computation, Volume 113, pp. 242–268. Supplementary material is available on mathrepo. 

Ch. Görgen, M. Leonelli, and O. Marigliano (2022). The curved exponential family of a staged tree. Electronic Journal of Statistics, Volume 16, pp. 2607–2620.

C. A. Guerra, M. Delgado‐Baquerizo, E. Duarte, O. Marigliano, Ch. Görgen, F. T. Maestre, and N. Eisenhauer (2021). Global projections of the soil microbiome in the Anthropocene. Global Ecology and Biogeography, Volume 30, pp. 987–999.

L. Carlini, N. Ay, and Ch. Görgen (2020). A numerical efficiency analysis of a common ancestor condition. In Mathematical Aspects of Computer and Information Sciences, MACIS 2019. Volume 11989 of Lecture Notes in Computer Science, Springer, pp. 357–363.

Ch. Görgen and M. Leonelli (2020). Model-preserving sensitivity analysis for families of Gaussian distributions. Journal of Machine Learning Research, Volume 21, pp. 1–32. Preprint available on arXiv:1809.10794 [stat.ML].

E. Duarte and Ch. Görgen (2020). Equations defining probability tree models. Journal of Symbolic Computation, Volume 99, pp. 127–146. Preprint available on arXiv:1802.04511 [math.ST].

Ch. Görgen, A. Bigatti, E. Riccomagno, and J. Q. Smith (2018). Discovery of statistical equivalence classes using computer algebra. International Journal of Approximate Reasoning, Volume 95, pp. 167–184. Preprint available on arXiv:1705.09457 [math.ST].

Ch. Görgen and J. Q. Smith (2018). Equivalence classes of staged trees. Bernoulli, Volume 24, Number 4A, pp. 2676–2692. Preprint available on arXiv:1512.00209v3 [math.ST].

M. Leonelli, Ch. Görgen, and J. Q. Smith (2017). Sensitivity analysis in multilinear probabilistic models. Information Sciences, Volume 411, pp. 84–97. Preprint available on arXiv:1512.02266 [cs.AI].

Ch. Görgen and J. Q. Smith (2016). A differential approach to causality in staged trees. In Proceedings of the Eighth International Conference on Probabilistic Graphical Models, Volume 52 of JMLR Workshop and Conference Proceedings, pp. 207–215.

Ch. Görgen, M. Leonelli, and J. Q. Smith (2015). A differential approach for staged trees. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Volume 9161 of Lecture Notes in Computer Science, Springer, pp. 346–355.

The first book on Chain Event Graph models

We have written a book! This is the first monograph on discrete statistical models which can be represented by coloured probability trees or, alternatively, so-called Chain Event Graphs: see the cover on the right. We present a number of new methods available for these models, including propagation algorithms, model selection techniques, and ongoing research on causal discovery algorithms. All of this is illustrated in a large number of small examples and one chapter-long analysis of a real dataset coming from a health study.

Rodrigo Abrunhosa Collazo, Christiane Görgen, and Jim Q. Smith (2018). Chain Event Graphs. Chapman & Hall/CRC Computer Science & Data Analysis Series.

My PhD thesis

I won the John Copas prize sponsored by the Faculty of Science of the University of Warwick for the most outstanding Statistics thesis amongst completed PhDs in 2017.

Ch. Görgen (2017). An algebraic characterisation of staged trees: their geometry and causal implications. PhD Thesis.