bayesian_hierarchical_modelling_of_noisy_gamma_processes

The code to reproduce the manuscript "Bayesian hierarchical modelling of noisy gamma processes: model formulation, identifiability, model fitting, and extensions to unit-to-unit variability".

https://github.com/rleadbett/bayesian_hierarchical_modelling_of_noisy_gamma_processes

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org, zenodo.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (6.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

The code to reproduce the manuscript "Bayesian hierarchical modelling of noisy gamma processes: model formulation, identifiability, model fitting, and extensions to unit-to-unit variability".

Basic Info
  • Host: GitHub
  • Owner: rleadbett
  • Language: HTML
  • Default Branch: main
  • Size: 43.6 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Citation

README.md

Bayesian hierarchical modelling of noisy gamma processes

DOI

This repository contains tex, code, and online appendix for

Leadbetter, R., Caceres, G. G., & Phatak, A. (2024). Bayesian Hierarchical Modelling of Noisy Gamma Processes: Model Formulation, Identifiability, Model Fitting, and Extensions to Unit-to-Unit Variability. ArXiv. /abs/2406.11216

Guide to repository

R/ Contains all code necessary to reproduce the main analysis. The .qmd files are Quarto documents with embedded R code and can be compiled by installing Quarto and R and running quarto render supplementary-material-1.qmd from the command line or by using RStudio.

figures/ All figures used to generate the main manuscript (generated by .qmd files).

paper/ All .tex code to generate the article in an arXiv template.

tables/ All tables used to generate the main manuscript in a LaTeX format (generated by .qmd files).

Owner

  • Login: rleadbett
  • Kind: user

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Code for 'Bayesian hierarchical modelling of noisy gamma
  processes: model formulation, identifiability, model
  fitting, and extensions to unit-to-unit variability'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Ryan
    family-names: Leadbetter
    email: ryan.leadbetter@postgrad.curtin.edu.au
    name-particle: ryan
    affiliation: >-
      Centre for Transforming Maintenance through Data
      Science, Curtin University
  - given-names: Gabriel
    family-names: Gonzalez Caceres
    email: G.Gonzalez@postgrad.curtin.edu.au
    affiliation: >-
      Centre for Transforming Maintenance through Data
      Science, Curtin University
  - given-names: Aloke
    family-names: Phatak
    email: Aloke.Phatak@curtin.edu.au
    affiliation: >-
      Centre for Transforming Maintenance through Data
      Science, Curtin University
identifiers:
  - type: doi
    value: 10.5281/zenodo.11900591
repository-code: >-
  https://github.com/rleadbett/Bayesian_hierarchical_modelling_of_noisy_gamma_processes
license: MIT
preferred-citation:
  type: article
  authors:
  - family-names: "Leadbetter"
    given-names: "Ryan"
  - family-names: "Gonzalez Caceres"
    given-names: "Gabriel"
  - family-names: "Phatak"
    given-names: "Aloke"
  journal: "ArXiv"
  volume: 2406.11216
  title: "Bayesian hierarchical modelling of noisy gamma processes: model formulation, identifiability, model fitting, and extensions to unit-to-unit variability"
  year: 2024
  url: https://arxiv.org/abs/2406.11216

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