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sips2022-efficientmodelevidencecomputation

Experiments and derivations for SiPS2022 paper "Efficient model evidence computation in tree-structured factor graph"

https://github.com/biaslab/sips2022-efficientmodelevidencecomputation

Science Score: 54.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    Low similarity (7.2%) to scientific vocabulary
Last synced: 7 months ago · JSON representation ·

Repository

Experiments and derivations for SiPS2022 paper "Efficient model evidence computation in tree-structured factor graph"

Basic Info
  • Host: GitHub
  • Owner: biaslab
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 293 KB
Statistics
  • Stars: 1
  • Watchers: 4
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme Citation

README.md

This repository contains experiments and derivations for the paper entitled

"Efficient Model Evidence Computation in Tree-structured Factor Graphs".

Setting up

Before implementing the experiments, we need to initialize an environment in Julia. This can be done by the following steps: * In a terminal, navigate to the location where you store the repository after cloning * type julia * type using Pkg, or ] * type Pkg.activate("."), or activate . if we use ] in the previous step. If you clone the repository and keep its name, you should see (SiPS2022-EfficientModeEvidenceComputation) pkg> in the terminal when you press ].

Now you can instantiate the project by Pkg.instantiate(), or instantiate if you press ]. This will install all necessary packages for the experiments.

Experiments

The repository contains 3 experiments located in 3 seperate files Coin_toss.ipynb, HMM.ipynb and LGSSM.ipynb. The experiments can be implemented by executing every code block in the corresponding files.

Supplementary document

We also include a supplement document sips2022_scalefactor_supplement.pdf which contains the derivation for all scale factor update rules in the paper.

Owner

  • Name: BIASlab
  • Login: biaslab
  • Kind: organization
  • Email: info@biaslab.org
  • Location: Eindhoven, the Netherlands

Bayesian Intelligent Autonomous Systems lab

Citation (Citation.cff) 

cff-version: 1.2.0
message: "Please cite this research as below."
authors:
- family-names: "Nguyen"
  given-names: "Hoang"
- family-names: "van Erp"
  given-names: "Bart"
  orcid: "https://orcid.org/0000-0002-5619-7071"
- family-names: "Senoz"
  given-names: "Ismail"
- family-names: "de Vries"
  given-names: "Bert"
title: "Efficient Model Evidence Computation in Tree-structured Factor Graphs"
version: 1.0.0
date-released: 2022-11
url: "https://github.com/biaslab/SiPS2022-EfficientModelEvidenceComputation"
preferred-citation:
  type: conference-paper
  authors:
  - family-names: "Nguyen"
    given-names: "Hoang"
  - family-names: "van Erp"
    given-names: "Bart"
    orcid: "https://orcid.org/0000-0002-5619-7071"
  - family-names: "Senoz"
    given-names: "Ismail"
  - family-names: "de Vries"
    given-names: "Bert"
  title: "Efficient Model Evidence Computation in Tree-structured Factor Graphs"
  year: 2022
  month: 11
  conference:
    - name: 2022 IEEE Workshop on Signal Processing Systems (SiPS)

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LENOVO m****g@t****l 11
Bart van Erp b****p@t****l 7
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tue.nl: 2

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