Suchergebnisse - (( (statneeeeeeeene OR stateeeeeeeee) python code analysis ) OR ( stat python code analysis ))*

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries.

Autoren: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. et al.

Quelle: Future Internet; Sep2025, Vol. 17 Issue 9, p412, 28p

Schlagwörter: LANGUAGE models, EXPERIMENTAL design, PYTHON programming language, GENERATIVE pre-trained transformers, QUANTITATIVE research, BENCHMARK problems (Computer science), CODE generators

Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python.

Autoren: Dunker, Fabian Mendoza, Emil Reale, Marco

Quelle: Mathematics (2227-7390); Dec2025, Vol. 13 Issue 23, p3764, 29p

Schlagwörter: PYTHON programming language, MAXIMUM likelihood statistics, CONSUMPTION (Economics), MATHEMATICAL programming, MULTILEVEL models, SCIENTIFIC computing

A statistical approach to automated analysis of the low‐contrast object detectability test for the large ACR MRI phantom.

Autoren: Golestani, Ali M. Gee, Julia M.

Quelle: Journal of Applied Clinical Medical Physics; Jul2025, Vol. 26 Issue 7, p1-9, 9p

Schlagwörter: LINEAR statistical models, IMAGING phantoms, QUALITY control, PYTHON programming language, MAGNETIC resonance imaging

Primary data for BJC Carbonic anhydrase IX is a pH-stat that sets an acidic tumour extracellular pH in vivo

Autoren: Griffiths, JR Lee, Shen-Han McIntyre, Dominick John et al.

Index Begriffe: Carbonic anhydrase IX, Extracellular pH, Intracellular pH, ISUCA, Magnetic Resonance Spectroscopy, MRS, pH-stat, Tumour pH, Dataset

URL: https://www.repository.cam.ac.uk/handle/1810/361916
https://doi.org/10.17863/CAM.26029
https://doi.org/10.1038/s41416-018-0216-5
https://www.repository.cam.ac.uk/handle/1810/287141
https://doi.org/10.1038/s41416-018-0216-5
https://www.repository.cam.ac.uk/handle/1810/287141

ImmobileParticles_MatchAndAnalyse: python code used for analysis of image data in Lenz et al. 2021, 'Measuring impacts of microplastic treatments via image recognition on immobilised particles below 100 µm'

Autoren: Lenz, Robin Enders, Kristina Fischer, Franziska et al.

Schlagwörter: microplastics microscopy image analysis python altair, scipo, stat

Relation: https://zenodo.org/record/4592320

Verfügbarkeit: https://zenodo.org/record/4592320

Python Programming: Numerical Analysis/Machine Learning Series.

Autoren: Doskas, Constantinos

Quelle: ISSA Journal; Oct2023, Vol. 21 Issue 10, p34-38, 5p

Schlagwörter: PYTHON programming language, NUMERICAL analysis, MACHINE learning

Uncertainty quantification for severe-accident reactor modelling: Results and conclusions of the MUSA reactor applications work package

Autoren: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Weitere Verfasser: Brumm, S. Gabrielli, F. Sanchez Espinoza, V. et al.

Quelle: ISSN: 0306-4549 ; Annals of Nuclear Energy ; https://hal.science/hal-04777159 ; Annals of Nuclear Energy, 2025, 211, pp.110962. ⟨10.1016/j.anucene.2024.110962⟩.

Schlagwörter: Nuclear reactor, Severe accident, Modelling, Uncertainty quantification, MUSA, Source Term, [PHYS]Physics [physics], [SPI]Engineering Sciences [physics], [STAT]Statistics [stat]

Relation: info:eu-repo/grantAgreement//847441/EU/Euratom research and training programme 2014 – 2018/Euratom

Verfügbarkeit: https://hal.science/hal-04777159
https://hal.science/hal-04777159v1/document
https://hal.science/hal-04777159v1/file/Final_version_ANE20225.pdf
https://doi.org/10.1016/j.anucene.2024.110962

Programmed differently? Testing for gender differences in Python programming style and quality on GitHub.

Autoren: Brooke, Siân

Quelle: Journal of Computer-Mediated Communication; Jan2024, Vol. 29 Issue 1, p1-13, 13p

Schlagwörter: PYTHON programming language, PYTHONS, RANDOM forest algorithms, GENDER

Automation of Software Code Analysis Using Machine Learning Methods.

Autoren: Moshkin, V. S. Dyrnochkin, A. A. Yarushkina, N. G.

Quelle: Pattern Recognition & Image Analysis; Sep2023, Vol. 33 Issue 3, p417-424, 8p

Entropy, Fidelity, and Entanglement During Digitized Adiabatic Quantum Computing to Form a Greenberger–Horne–Zeilinger (GHZ) State.

Autoren: Jansen, Nathan D. Hunt, Katharine L. C.

Quelle: Entropy; Sep2025, Vol. 27 Issue 9, p891, 21p

Schlagwörter: ENTROPY, QUANTUM entanglement, STATISTICAL accuracy, HAMILTONIAN mechanics, ISING model, QUANTUM computing, QUANTUM computers

Firma/Körperschaft: INTERNATIONAL Business Machines Corp.

Py Code for Stats and Vis in Isotopic Evidence for Interseasonal Bioaccumulation of Perfluoroalkyl Substances (PFASs) in Icelandic seabirds

Autoren: Shen, Rui Larsen, Thomas

Relation: https://zenodo.org/records/10120463; oai:zenodo.org:10120463; https://doi.org/10.5281/zenodo.10120463

Verfügbarkeit: https://doi.org/10.5281/zenodo.10120463
https://zenodo.org/records/10120463

Unsupervised Voting for Detecting the Algorithmic Solving Strategy in Competitive Programming Solutions.

Autoren: Stoica, Alexandru Stefan Babiceanu, Daniel Mihaescu, Marian Cristian et al.

Quelle: Mathematics (2227-7390); Nov2025, Vol. 13 Issue 22, p3589, 19p

Schlagwörter: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code

An extensive study of the effects of different deep learning models on code vulnerability detection in Python code.

Autoren: Wang, Rongcun Xu, Senlei Ji, Xingyu et al.

Quelle: Automated Software Engineering; Jun2024, Vol. 31 Issue 1, p1-35, 35p

A comparison of machine learning algorithms for survival with missing data ; Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes

Autoren: Dufossé, Paul Benzekry, Sébastien Aix Marseille Université (AMU) et al.

Weitere Verfasser: Dufossé, Paul Benzekry, Sébastien Aix Marseille Université (AMU) et al.

Quelle: 54es Journées de Statistique de la SFdS
JDS 2023 - 54e Journées de Statistique de la SFds
https://hal.science/hal-04384220
JDS 2023 - 54e Journées de Statistique de la SFds, SFdS, Jul 2023, Bruxelles (BEL), Belgique. ⟨10.48550/arXiv.2303.13590⟩

Schlagwörter: Machine Learning (stat.ML), Machine Learning (cs.LG), Machine learning, neural networks, survival data, simulated data, missing data, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.AP]Statistics [stat]/Applications [stat.AP]

Geographisches Schlagwort: Bruxelles (BEL), Belgium

Verfügbarkeit: https://hal.science/hal-04384220
https://hal.science/hal-04384220v1/document
https://hal.science/hal-04384220v1/file/preprint_arxiv.pdf
https://doi.org/10.48550/arXiv.2303.13590

Time Series Analysis with Python Cookbook : Practical Recipes for the Complete Time Series Workflow, From Modern Data Engineering to Advanced Forecasting and Anomaly Detection

Autoren: Tarek A. Atwan

Resource Type: eBook.

Categories: COMPUTERS / Data Science / Data Modeling & Design, COMPUTERS / Data Science / General, COMPUTERS / Data Science / Data Analytics

GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model.

Autoren: Li, Jingmao Xu, Yaqing Ma, Shuangge et al.

Quelle: Bioinformatics; Sep2025, Vol. 41 Issue 9, p1-9, 9p

Schlagwörter: CANCER research, GENOTYPE-environment interaction, DIAGNOSTIC imaging, DEEP learning, EMPIRICAL research, COMPUTER simulation

An optimization method for klystron magnetic focusing system.

Autoren: Wang, Yiao Zhou, Zusheng Xiao, Ouzheng et al.

Quelle: Radiation Detection Technology & Methods; Dec2025, Vol. 9 Issue 4, p662-671, 10p

DeepSeek-V3, GPT-4, Phi-4, and LLaMA-3.3 Generate Correct Code for LoRaWAN-Related Engineering Tasks.

Autoren: Fernandes, Daniel Matos-Carvalho, João P. Fernandes, Carlos M. et al.

Quelle: Electronics (2079-9292); Apr2025, Vol. 14 Issue 7, p1428, 24p

Schlagwörter: LANGUAGE models, GENERATIVE pre-trained transformers, GENERATING functions, RESEARCH questions, NATURAL languages

Python Programming Log Analysis Series.

Autoren: Doskas, Constantinos

Quelle: ISSA Journal; Oct2021, Vol. 19 Issue 10, p20-23, 4p

Schlagwörter: PYTHON programming language, SAWLOGS, RECORDS management, INFORMATION assurance, DATABASES, TEXT files

A Short Guide to Using Python For Data Analysis In Experimental Physics

Autoren: Fortune, Nathanael Alexander

Quelle: Physics: Faculty Publications

Schlagwörter: DATA ANALYSIS, NUMPY, PHYSICS, PYTHON, SCIPY, Computer Sciences, stat, edu

Relation: https://scholarworks.smith.edu/phy_facpubs/30

Verfügbarkeit: https://scholarworks.smith.edu/phy_facpubs/30