https://github.com/biaslab/onlinemessagepassingdirichletprocess
https://github.com/biaslab/onlinemessagepassingdirichletprocess
Science Score: 26.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Scientific vocabulary similarity
Low similarity (8.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: biaslab
- Language: Julia
- Default Branch: main
- Size: 34.2 KB
Statistics
- Stars: 3
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Online Structure Learning with Dirichlet Processes through Message Passing
By Bart van Erp, Wouter W. L. Nuijten and Bert de Vries
Abstract Generative or probabilistic modeling is crucial for developing intelligent agents that can reason about their environment. However, designing these models manually for complex tasks is often infeasible. Structure learning addresses this challenge by automating model creation based on sensory observations, balancing accuracy with complexity. Central to structure learning is Bayesian model comparison, which provides a principled framework for evaluating models based on their evidence. This paper focuses on model expansion and introduces an online message passing procedure using Dirichlet processes, a prominent prior in non-parametric Bayesian methods. Our approach builds on previous work by automating Bayesian model comparison using message passing based on variational free energy minimization. We derive novel message passing update rules to emulate Dirichlet processes, offering a flexible and scalable method for online structure learning. Our method generalizes to arbitrary models and treats structure learning identically to state estimation and parameter learning. The experimental results validate the effectiveness of our approach on an infinite mixture model.
This repository contains all experiments of the paper.
Installation instructions
Install Julia
Start a Pluto.jl server ```sh
make pluto ```
License
MIT License Copyright (c) 2024 BIASlab
Owner
- Name: BIASlab
- Login: biaslab
- Kind: organization
- Email: info@biaslab.org
- Location: Eindhoven, the Netherlands
- Website: http://biaslab.org
- Repositories: 47
- Profile: https://github.com/biaslab
Bayesian Intelligent Autonomous Systems lab
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Bart van Erp | 4****p | 10 |
| Bagaev Dmitry | b****i@g****m | 1 |
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Total issue authors: 0
- Total pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 1
- Average time to close issues: N/A
- Average time to close pull requests: less than a minute
- Issue authors: 0
- Pull request authors: 1
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
- bartvanerp (1)