A visual analytics workflow for probabilistic modeling

Author:

Klaus JulienORCID,Blacher Mark,Goral Andreas,Lucas Philipp,Giesen Joachim

Funder

Carl-Zeiss-Stiftung

Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Human-Computer Interaction,Software

Reference49 articles.

1. Arora, N.S., Russell, S.J., Kidwell, P., Sudderth, E.B., 2010. Global seismic monitoring as probabilistic inference. In: Advances in Neural Information Processing Systems. NIPS, pp. 73–81.

2. Arora, N.S., Russell, S.J., Kidwell, P., Sudderth, E.B., 2011. Global Seismic Monitoring: A Bayesian Approach. In: Proceedings of the Conference on Artificial Intelligence. AAAI.

3. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI;Arrieta;Inf. Fusion,2020

4. Pyro: Deep universal probabilistic programming;Bingham;J. Mach. Learn. Res.,2019

5. Blackwell, A.F., Church, L., Erwig, M., Geddes, J., Gordon, A., Gorinova, M., Baydin, A.G., Gram-Hansen, B., Kohn, T., Lawrence, N., Mansinghka, V., Paige, B., Petricek, T., Robinson, D., Sarkar, A., Strickson, O., 2019. Usability of Probabilistic Programming Languages. In: Proceedings of the Workshop of the Psychology of Programming Interest Group. PPIG.

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2. Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting;2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT);2024-02-19

3. Causality-Based Visual Analysis of Questionnaire Responses;IEEE Transactions on Visualization and Computer Graphics;2023

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