{"id":25790678,"url":"https://github.com/danielwohlr/zikkurat-implementation","last_synced_at":"2026-05-26T16:32:43.703Z","repository":{"id":217028520,"uuid":"742964494","full_name":"Danielwohlr/zikkurat-implementation","owner":"Danielwohlr","description":"Matlab implementation of RNG Zikkurat method. Finished 2020","archived":false,"fork":false,"pushed_at":"2025-01-06T13:56:35.000Z","size":1028,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-27T15:08:57.201Z","etag":null,"topics":["fallback","generalized-inversed-gaussian","matlab","rng"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Danielwohlr.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2024-01-13T22:22:02.000Z","updated_at":"2025-01-07T08:41:38.000Z","dependencies_parsed_at":"2024-01-14T07:02:17.852Z","dependency_job_id":"a9d7bfe5-0750-41b4-b1e1-745803a27530","html_url":"https://github.com/Danielwohlr/zikkurat-implementation","commit_stats":null,"previous_names":["danielwohlr/zikkurat-implementation"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Danielwohlr/zikkurat-implementation","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Danielwohlr%2Fzikkurat-implementation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Danielwohlr%2Fzikkurat-implementation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Danielwohlr%2Fzikkurat-implementation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Danielwohlr%2Fzikkurat-implementation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Danielwohlr","download_url":"https://codeload.github.com/Danielwohlr/zikkurat-implementation/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Danielwohlr%2Fzikkurat-implementation/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33529477,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"ssl_error","status_checked_at":"2026-05-26T15:22:15.568Z","response_time":63,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["fallback","generalized-inversed-gaussian","matlab","rng"],"created_at":"2025-02-27T12:07:56.728Z","updated_at":"2026-05-26T16:32:43.687Z","avatar_url":"https://github.com/Danielwohlr.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Custom RNG implementation using Zikkurat method in MATLAB \n\nThis report presents the implementation of the Ziggurat method for generating pseudorandom numbers using MATLAB. It extends the method to both Normal (Gaussian) and Generalized Inverse Gaussian (GIG) distributions, exploring algorithmic adaptations for these specific cases. The GIG distribution is of the form\n$$f(x) = \\frac{(\\frac{a}{b})^{p/2}}{2 \\mathcal{K}_p(\\sqrt{ab})} \\Theta(x) x^{p-1} \\mathrm{exp}\\left(-\\frac{ax +\\frac{b}{x}}{2}\\right),$$\n\nwhere $\\Theta(x)$ is the Heaviside step function, $\\mathcal{K}_p$ is the modified Bessel function of the second kind, and the parameters are $a,b\u003e0, p \\in \\mathbb{R}$.\n\nThe final report is in ```english-version.pdf```. \n\n![Project Screenshot](images/drawing_ziggurat.svg \"Implementation of Zikkurat method\")\n![Project Screenshot](images/zikkurat.jpeg \"Zikkurat building\")\n\n## Table of Contents\n- [GAUSS](#gauss)\n- [GIG](#gig)\n- [Images](#images)\n\n## GAUSS\n\nContains MATLAB scripts for the Zikkurat algorithm (rejection rules, statistical checks, fallback, etc.) implementation in the case of the Normal (Gaussian) distribution.\n\n## GIG\n\nContains MATLAB scripts for the Zikkurat algorithm (rejection rules, statistical checks, fallback, etc.) implementation in the case of the GIG distribution.\n\n## Images\n\nAll the generated images are saved in the directory ```images```. It is usually not a good practice to upload images to github.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanielwohlr%2Fzikkurat-implementation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanielwohlr%2Fzikkurat-implementation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanielwohlr%2Fzikkurat-implementation/lists"}