PyMC: Bayesian Stochastic Modelling in Python.

399 indexed citations

Abstract

loading...

About

This paper, published in 2010, received 399 indexed citations. Written by Anand P. Patil, David Huard and Christopher Fonnesbeck covering the research area of Statistical and Nonlinear Physics and Artificial Intelligence. It is primarily cited by scholars working on Astronomy and Astrophysics (65 citations), Artificial Intelligence (59 citations) and Molecular Biology (39 citations). Published in PubMed.

In The Last Decade

doi.org/w41846674 →

Countries where authors are citing PyMC: Bayesian Stochastic Modelling in Python.

Specialization
Citations

This map shows the geographic impact of PyMC: Bayesian Stochastic Modelling in Python.. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by PyMC: Bayesian Stochastic Modelling in Python. with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites PyMC: Bayesian Stochastic Modelling in Python. more than expected).

Fields of papers citing PyMC: Bayesian Stochastic Modelling in Python.

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of PyMC: Bayesian Stochastic Modelling in Python.. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the PyMC: Bayesian Stochastic Modelling in Python..

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w41846674.

Explore hit-papers with similar magnitude of impact

Breakdown of academic impact, for the paper Practice Guidelines for Obstetric Anesthesia Breakdown of academic impact, for the paper Periprocedural Bleeding and Thromboembolic Events With Dabigatran Compared With Warfarin Breakdown of academic impact, for the paper Contemporary H3N2 influenza viruses have a glycosylation site that alters binding of antibodies elicited by egg-adapted vaccine strains Breakdown of academic impact, for the paper Mechanism of SARS-CoV-2 polymerase stalling by remdesivir Breakdown of academic impact, for the paper The genomic landscape underlying phenotypic integrity in the face of gene flow in crows Breakdown of academic impact, for the paper Propranolol for Severe Infantile Hemangiomas: Follow-Up Report Breakdown of academic impact, for the paper Perivascular M2 Macrophages Stimulate Tumor Relapse after Chemotherapy Breakdown of academic impact, for the paper Efficacy of PD-1 Blockade Is Potentiated by Metformin-Induced Reduction of Tumor Hypoxia Breakdown of academic impact, for the paper Identification of Novel Mobilized Colistin Resistance Gene mcr-9 in a Multidrug-Resistant, Colistin-Susceptible Salmonella enterica Serotype Typhimurium Isolate Breakdown of academic impact, for the paper Imputation with the R Package VIM
Rankless by CCL
2026