{"id":24158132,"url":"https://github.com/ulizesr/ift","last_synced_at":"2025-03-02T01:26:45.933Z","repository":{"id":258397356,"uuid":"862068612","full_name":"UlizesR/IFT","owner":"UlizesR","description":"Repo to hold my research on Information Field Theory code","archived":false,"fork":false,"pushed_at":"2024-12-02T03:54:56.000Z","size":2199,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-12T14:19:47.068Z","etag":null,"topics":["bayesian-inference","information-theory","python"],"latest_commit_sha":null,"homepage":"","language":null,"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/UlizesR.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,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-24T01:34:49.000Z","updated_at":"2024-12-02T03:55:00.000Z","dependencies_parsed_at":"2024-10-18T20:15:59.488Z","dependency_job_id":"7c53eb1a-f42c-470f-b271-bf771cea384a","html_url":"https://github.com/UlizesR/IFT","commit_stats":null,"previous_names":["ulizesr/ift_code"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UlizesR%2FIFT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UlizesR%2FIFT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UlizesR%2FIFT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UlizesR%2FIFT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/UlizesR","download_url":"https://codeload.github.com/UlizesR/IFT/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241446197,"owners_count":19964136,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["bayesian-inference","information-theory","python"],"created_at":"2025-01-12T14:19:49.284Z","updated_at":"2025-03-02T01:26:45.912Z","avatar_url":"https://github.com/UlizesR.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Information Field Theory Research Repository\n\nWelcome to the Information Field Theory (IFT) Research Repository. This repository contains our work exploring alternative methods for inferring the moments of non-Gaussian continuous random fields using techniques derived from Information Field Theory.\n\n## Overview\n\nIn various fields like physics, astronomy, and engineering, many problems involve continuous random fields that exhibit spatial and temporal variations. While Gaussian random fields are well-understood and can be analyzed by inferring their mean and variance, non-Gaussian fields present significant challenges due to the computational complexity involved in traditional inference methods.\n\nThis project focuses on:\n\n- **Alternative Inference Methods**: Developing and applying diagrammatic expansion techniques from Information Field Theory to compute the moments of non-Gaussian fields more efficiently.\n- **Bayesian Inference Challenges**: Addressing the difficulties in calculating the evidence term in Bayes' theorem for continuous, non-parameterized fields.\n- **Reducing Computational Complexity**: Providing methods that are less computationally intensive than traditional techniques like Markov Chain Monte Carlo (MCMC), especially as the number of parameters increases.\n- **Application to Practical Problems**: Extending these techniques to practical cases such as linear regression models and exploring their effectiveness.\n\n## Key Components\n\n- **Diagrammatic Expansion Technique**: Utilizing a method that represents posterior moments as an infinite sum of diagrams, each corresponding to specific equations based on their structure.\n- **Hamiltonian Formalism**: Adopting concepts from statistical mechanics, such as the Gibbs measure and partition function, to reformulate the inference problem.\n- **Perturbatively Non-Gaussian Fields**: Focusing on fields that deviate slightly from Gaussian distributions, making them suitable for perturbative methods.\n\n## Future Work\n\n- **Higher-Dimensional Applications**: Extending the techniques to fields defined over continuous, higher-dimensional spaces.\n- **Basis Functions**: Investigating the use of basis functions to improve computational efficiency and simplify calculations.\n- **Broader Distribution Testing**: Exploring the applicability of these methods to other named distributions beyond the Gaussian case.\n\n## Conclusion\n\nThis repository aims to provide valuable insights and tools for researchers dealing with the complexities of non-Gaussian continuous random fields. By leveraging Information Field Theory and diagrammatic expansions, we strive to develop more efficient and scalable inference methods that overcome the limitations of traditional computational techniques.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fulizesr%2Fift","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fulizesr%2Fift","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fulizesr%2Fift/lists"}