Search Results - (( (stavebneeenei OR stateni) python code analysis ) OR ( stat python code analysis ))*

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

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

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

Subject Terms: 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.

Authors: Dunker, Fabian Mendoza, Emil Reale, Marco

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

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

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

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

Index Terms: 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'

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

Subject Terms: microplastics microscopy image analysis python altair, scipo, stat

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

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

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

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

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

Source: 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⟩.

Subject Terms: 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

Availability: 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

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

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

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

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

Company/Entity: INTERNATIONAL Business Machines Corp.

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

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

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

Source: 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⟩

Subject Terms: Machine Learning (cs.LG), Machine Learning (stat.ML), 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]

Subject Geographic: Bruxelles (BEL), Belgium

Availability: 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

Authors: Tarek A. Atwan

Resource Type: eBook.

Subjects: Python (Computer program language), Machine learning

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.

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

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

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

An optimization method for klystron magnetic focusing system.

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

Source: 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.

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

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

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

Python Programming Log Analysis Series.

Authors: Doskas, Constantinos

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

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

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

Authors: Fortune, Nathanael Alexander

Source: Physics: Faculty Publications

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

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

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

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

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

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

Subject Terms: MACHINE learning, CLUSTERING algorithms, COMPUTER science, ALGORITHMS, SOURCE code

Data and code for project 'What makes users click: The effect of news values negativity and surprise in news headlines on selection likelihood'

Authors: Femke Kok Emily Kruidhof

Subject Terms: stat, info

Relation: https://zenodo.org/record/5940294

Availability: https://zenodo.org/record/5940294

Portable Electronic Olfactometer for Non-Invasive Screening of Canine Ehrlichiosis: A Proof-of-Concept Study Using Machine Learning.

Authors: Durán Cotrina, Silvana Valentina Acevedo, Cristhian Manuel Durán Carrillo Gómez, Jeniffer Katerine

Source: Veterinary Sciences. Jan2026, Vol. 13 Issue 1, p88. 20p.

Zinc as a Biomarker of Nutritional Status and Clinical Burden in Recessive Dystrophic Epidermolysis Bullosa: Implications for Preventive Monitoring.

Authors: Quintana-Castanedo, Lucía Maseda, Rocío Sánchez-Ramón, Silvia et al.

Source: Nutrients. Jan2026, Vol. 18 Issue 2, p232. 19p.

Compound-SNE: comparative alignment of t-SNEs for multiple single-cell omics data visualization.

Authors: Cess, Colin G Haghverdi, Laleh

Source: Bioinformatics; Jul2024, Vol. 40 Issue 7, p1-5, 5p

Subject Terms: RESEARCH personnel, DATA visualization, GENE expression, DATA analysis, PYTHON programming language

Towards the genome-scale discovery of bivariate monotonic classifiers.

Authors: Fourquet, Océane Krejca, Martin S. Doerr, Carola et al.

Source: BMC Bioinformatics; 9/2/2025, Vol. 26 Issue 1, p1-32, 32p

Subject Terms: CLASSIFICATION, BIOMARKERS, ALGORITHMS, MACHINE learning, BIOINFORMATICS

pyjeo: A Python Package for the Analysis of Geospatial Data

Authors: Pieter Kempeneers Ondrej Pesek Davide De Marchi et al.

Source: ISPRS International Journal of Geo-Information, Vol 8, Iss 10, p 461 (2019)

Subject Terms: open-source software, geospatial data, image processing, stat, geo

Relation: https://doaj.org/article/352223c398b04d87ade0a49b91244b53

Availability: https://doi.org/10.3390/ijgi8100461
https://doaj.org/article/352223c398b04d87ade0a49b91244b53