[PRE REVIEW]: 'SBIAX: Density-estimation simulation-based inference in JAX.'

[editorialbot]

Submitting author: @homerjed (Jed Homer)
Repository: https://github.com/homerjed/sbiax
Branch with paper.md (empty if default branch):
Version: 0.0.9
Editor: @jbytecode
Reviewers: @kazewong, @maja-jablonska
Managing EiC: Chris Vernon

Status

Status badge code:

HTML: <a href="https://joss.theoj.org/papers/e4c4fd0dacac8b25e2d07ea4144db314"><img src="https://joss.theoj.org/papers/e4c4fd0dacac8b25e2d07ea4144db314/status.svg"></a>
Markdown: [![status](https://joss.theoj.org/papers/e4c4fd0dacac8b25e2d07ea4144db314/status.svg)](https://joss.theoj.org/papers/e4c4fd0dacac8b25e2d07ea4144db314)

Author instructions

Thanks for submitting your paper to JOSS @homerjed. Currently, there isn't a JOSS editor assigned to your paper.

@homerjed if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). You can search the list of people that have already agreed to review and may be suitable for this submission.

Editor instructions

The JOSS submission bot @editorialbot is here to help you find and assign reviewers and start the main review. To find out what @editorialbot can do for you type:

@editorialbot commands

[editorialbot]

Hello human, I'm @editorialbot, a robot that can help you with some common editorial tasks.

For a list of things I can do to help you, just type:

@editorialbot commands

For example, to regenerate the paper pdf after making changes in the paper's md or bib files, type:

@editorialbot generate pdf

[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.90  T=0.04 s (1089.3 files/s, 155006.7 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
Python                          30            555            748           2116
Jupyter Notebook                 3              0           1100            536
YAML                             3             20              8            170
Markdown                         2             71              0            151
TeX                              1             17              0            145
TOML                             1              3              0             52
-------------------------------------------------------------------------------
SUM:                            40            666           1856           3170
-------------------------------------------------------------------------------

Commit count by author:

    43	homerjed

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

✅ OK DOIs

- 10.5281/zenodo.10402073 is OK
- 10.1073/pnas.1912789117 is OK
- 10.1093/mnras/stz1960 is OK

🟡 SKIP DOIs

- No DOI given, and none found for title: Variational Inference with Normalizing Flows
- No DOI given, and none found for title: FFJORD: Free-form Continuous Dynamics for Scalable...
- No DOI given, and none found for title: Neural Ordinary Differential Equations
- No DOI given, and none found for title: BlackJAX: Composable Bayesian inference in JAX
- No DOI given, and none found for title: Masked Autoregressive Flow for Density Estimation
- No DOI given, and none found for title: Flow Matching for Generative Modeling
- No DOI given, and none found for title: Optuna: A Next-Generation Hyperparameter Optimizat...
- No DOI given, and none found for title: Neural Density Estimation and Likelihood-free Infe...
- No DOI given, and none found for title: Automatic Posterior Transformation for Likelihood-...
- No DOI given, and none found for title: JAX: composable transformations of Python+NumPy pr...
- No DOI given, and none found for title: Equinox: neural networks in JAX via callable PyTre...
- No DOI given, and none found for title: The DeepMind JAX Ecosystem
- No DOI given, and none found for title: On Neural Differential Equations
- No DOI given, and none found for title: Simulation-Based Inference Has It’s Own Dodelson-S...

❌ MISSING DOIs

- None

❌ INVALID DOIs

- None

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 1378

✅ The paper includes a Statement of need section

[editorialbot]

⚠️ An error happened when generating the pdf. Problem with ORCID (0000-0000-0000-0000) for Jed Homer. Invalid ORCID.

[editorialbot]

License info:

✅ License found: MIT License (Valid open source OSI approved license)

[homerjed]

@editorialbot generate pdf

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[editorialbot]

Five most similar historical JOSS papers:

BayesFlow: Amortized Bayesian Workflows With Neural Networks
Submitting author: @marvinschmitt
Handling editor: @osorensen (Active)
Reviewers: @sandeshkatakam, @LoryPack
Similarity score: 0.7750

swyft: Truncated Marginal Neural Ratio Estimation in Python
Submitting author: @bkmi
Handling editor: @pdebuyl (Active)
Reviewers: @mattpitkin, @olgadoronina
Similarity score: 0.7672

BlackBIRDS: Black-Box Inference foR Differentiable Simulators
Submitting author: @arnauqb
Handling editor: @rkurchin (Active)
Reviewers: @rajeshrinet, @marvinschmitt
Similarity score: 0.7451

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX
Submitting author: @kazewong
Handling editor: @rkurchin (Active)
Reviewers: @matt-graham, @Daniel-Dodd
Similarity score: 0.7409

Re-Envisioning Numerical Information Field Theory (NIFTy.re): A Library for Gaussian Processes and Variational Inference
Submitting author: @Edenhofer
Handling editor: @dfm (Active)
Reviewers: @Abinashbunty, @apizzuto
Similarity score: 0.7154

⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before considering asking the reviewers of these papers to review again for JOSS.

[crvernon]

@homerjed - I noticed that your repository is only a few weeks old. Can you discuss the timeline of this project a bit here in this thread a bit? Thanks.

[homerjed]

Hi @crvernon,

This repository was pushed from code I wrote in preparation for a project about challenging the use of density estimation SBI in cosmological inference problems.

Whilst it's only a few weeks old I have been working on this code and project for about a year. Now seemed like a good time to push the code and publish (a JOSS paper + astrophysics journal paper) since I want the readers of my paper (to be Arxiv'd in the next week or so) to run my experiments themselves.

Additionally, the code is ideal for further independent SBI analyses and beyond - personally I found other existing codebases unwieldly.

Does that cover it? Let me know, cheers :)

[crvernon]

Thanks @homerjed

[crvernon]

@editorialbot invite @RMeli as editor

👋 @RMeli are you able to take this one on as editor?

[editorialbot]

Invitation to edit this submission sent!

[crvernon]

Just a reminder of the above @RMeli

[RMeli]

Hi @crvernon, thank you for the ping, I missed this notification. Sorry about that! Unfortunately I'm quite swamped in this period and I would not be able to ensure a timely review.

[crvernon]

No problem. Thanks @RMeli.

[crvernon]

@editorialbot invite @jbytecode as editor

👋 @jbytecode - do you think you could take this one on as editor?

[editorialbot]

Invitation to edit this submission sent!

[jbytecode]

@editorialbot assign me as editor

@crvernon - sure, gladly!

[editorialbot]

Assigned! @jbytecode is now the editor

[jbytecode]

@editorialbot check references

[editorialbot]

Reference check summary (note 'MISSING' DOIs are suggestions that need verification):

✅ OK DOIs

- 10.5281/zenodo.10402073 is OK
- 10.1073/pnas.1912789117 is OK
- 10.1093/mnras/stz1960 is OK

🟡 SKIP DOIs

- No DOI given, and none found for title: Variational Inference with Normalizing Flows
- No DOI given, and none found for title: FFJORD: Free-form Continuous Dynamics for Scalable...
- No DOI given, and none found for title: Neural Ordinary Differential Equations
- No DOI given, and none found for title: BlackJAX: Composable Bayesian inference in JAX
- No DOI given, and none found for title: Masked Autoregressive Flow for Density Estimation
- No DOI given, and none found for title: Flow Matching for Generative Modeling
- No DOI given, and none found for title: Optuna: A Next-Generation Hyperparameter Optimizat...
- No DOI given, and none found for title: Neural Density Estimation and Likelihood-free Infe...
- No DOI given, and none found for title: Automatic Posterior Transformation for Likelihood-...
- No DOI given, and none found for title: JAX: composable transformations of Python+NumPy pr...
- No DOI given, and none found for title: Equinox: neural networks in JAX via callable PyTre...
- No DOI given, and none found for title: The DeepMind JAX Ecosystem
- No DOI given, and none found for title: On Neural Differential Equations
- No DOI given, and none found for title: Simulation-Based Inference Has It’s Own Dodelson-S...

❌ MISSING DOIs

- None

❌ INVALID DOIs

- None

[jbytecode]

@editorialbot generate pdf

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[editorialbot]

Five most similar historical JOSS papers:

BayesFlow: Amortized Bayesian Workflows With Neural Networks
Submitting author: @marvinschmitt
Handling editor: @osorensen (Active)
Reviewers: @sandeshkatakam, @LoryPack
Similarity score: 0.7600

swyft: Truncated Marginal Neural Ratio Estimation in Python
Submitting author: @bkmi
Handling editor: @pdebuyl (Active)
Reviewers: @mattpitkin, @olgadoronina
Similarity score: 0.7567

flowMC: Normalizing flow enhanced sampling package for probabilistic inference in JAX
Submitting author: @kazewong
Handling editor: @rkurchin (Active)
Reviewers: @matt-graham, @Daniel-Dodd
Similarity score: 0.7337

BlackBIRDS: Black-Box Inference foR Differentiable Simulators
Submitting author: @arnauqb
Handling editor: @rkurchin (Active)
Reviewers: @rajeshrinet, @marvinschmitt
Similarity score: 0.7327

pocoMC: A Python package for accelerated Bayesian inference in astronomy and cosmology
Submitting author: @minaskar
Handling editor: @dfm (Active)
Reviewers: @kazewong, @marylou-gabrie
Similarity score: 0.7078

⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before considering asking the reviewers of these papers to review again for JOSS.

[jbytecode]

@homerjed - Hi, thank you for sending your work to JOSS. I'm the handling editor of this submission.

Do you have any suggestions for potential reviewers?

The editorial bot suggests some recently published works and their corresponding authors. You can investigate them if they are similar to your work. We can invite their authors as reviewers.

You can also use the list of people (https://reviewers.joss.theoj.org/lookup) for searching suitable reviewers.

If so, please provide me a list of GitHub handles. Please don't use the @ character for avoding unwanted notifications.

Thank you in advance.

[homerjed]

Hi @jbytecode,

I would suggest possibly kazewong or mwalmsley?

Cheers :)

[jbytecode]

👋👋👋 Dear @kazewong, @mwalmsley 👋👋👋

Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)?

JOSS publishes articles about open source research software. The submission I'd like you to review is titled:

SBIAX: Density-estimation simulation-based inference in JAX.

You can find more information at the top of this Github issue (#7429).

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know.

This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be 23 check items for each single reviewer. Reviewing in JOSS is well documented and is available online: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html

Thank you in advance!

[jbytecode]

@homerjed - We can invite someone else in this stage? Do you have any suggestions?

[kazewong]

Sorry I missed this, but I can review this in the coming couple of weeks if it is still desired

[homerjed]

That would be very nice, if you are available, thank you 🙏 !

[mwalmsley]

Hi @jbytecode , I'm sorry but I don't think I have capacity to help with this right now (and I'm also not much of an SBI or JAX expert). The Euclid Q1 splash papers are due in Jan and that's taking a lot of my time. I hope you find someone else (I might suggest Maya

[jbytecode]

@editorialbot add @kazewong as reviewer

[editorialbot]

@kazewong added to the reviewers list!

[jbytecode]

👋👋👋 Dear @maja-jablonska 👋👋👋

I'm writing to you here due to the suggestion of @mwalmsley

Would you be willing to assist in reviewing this submission for JOSS (Journal of Open Source Software)?

JOSS publishes articles about open source research software. The submission I'd like you to review is titled:

SBIAX: Density-estimation simulation-based inference in JAX.

You can find more information at the top of this Github issue (#7429).

The review process at JOSS is unique: it takes place in a GitHub issue, is open, and author-reviewer-editor conversations are encouraged. If you have any questions please let me know.

This is the pre-review issue. After setting at least 2 reviewers we will start the review process in a separate thread. In that thread, there will be 23 check items for each single reviewer. Reviewing in JOSS is well documented and is available online: https://joss.readthedocs.io/en/latest/reviewer_guidelines.html

Thank you in advance!

[maja-jablonska]

Hi @jbytecode, apologies for my late reply!! I was a bit sick so I didn't check GitHub. Thank you so much for suggesting me @mwalmsley , I am happy to help!

[jbytecode]

@maja-jablonska - thank you for accepting our invitation.

@kazewong, @maja-jablonska - I'm now starting the review in a separate thread. I'll be introducing the review instructions there.

[jbytecode]

@editorialbot add @maja-jablonska as reviewer

[editorialbot]

@maja-jablonska added to the reviewers list!

[jbytecode]

@editorialbot start review

[editorialbot]

OK, I've started the review over in #7606.

[editorialbot]

I'm sorry @maja-jablonska, I'm afraid I can't do that. That's something only editors are allowed to do.