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Joss paper #242

Merged
merged 2 commits into from
Jan 12, 2024
Merged

Joss paper #242

merged 2 commits into from
Jan 12, 2024

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odunbar
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@odunbar odunbar commented Oct 26, 2023

Purpose

Closes #245
Closes #261

Content

To Find generated PDF


  • I have read and checked the items on the review checklist.

@odunbar odunbar force-pushed the JOSS-paper branch 2 times, most recently from 256078b to a45a600 Compare December 12, 2023 18:36
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codecov bot commented Dec 12, 2023

Codecov Report

All modified and coverable lines are covered by tests ✅

Comparison is base (a621a04) 88.09% compared to head (a6db621) 88.08%.
Report is 11 commits behind head on main.

Additional details and impacted files
@@            Coverage Diff             @@
##             main     #242      +/-   ##
==========================================
- Coverage   88.09%   88.08%   -0.01%     
==========================================
  Files           7        8       +1     
  Lines        1134     1167      +33     
==========================================
+ Hits          999     1028      +29     
- Misses        135      139       +4     

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@odunbar odunbar force-pushed the JOSS-paper branch 2 times, most recently from f87ef10 to 11dd2f8 Compare January 5, 2024 18:34
@lm2612
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lm2612 commented Jan 5, 2024

on the ABC part
L65 "these tools use statistical emulators to accelerate sampling from a posterior. ABC learns the likelihood directly from the data"
I could be wrong here but my understanding of ABC was that it doesn't necessarily require an emulator, although looking at gpABC they have the option to use GP to accelerate it. The phrasing "ABC learns the likelihood directly from the data" also confused me slightly as it is normally described as "likelihood free"

Something along the lines of:
"these tools both approximately sample from the posterior distribution. In ABC, this approximation comes from bypassing the likelihood that is usually required in sampling methods, such as MCMC. Instead, the objective of ABC is that model output agrees with data based on a scalar-valued objective. In CES, the approximation comes from learning the parameter-to-data map, then following this it calculates an explicit likelihood and uses exact sampling via MCMC. Some ABC algorithms also make use of statistical emulators to further accelerate sampling (gpABC)."

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odunbar commented Jan 9, 2024

This is a fair comment! I have adjusted the paragraph.

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odunbar commented Jan 9, 2024

NB there are Julia-wide registry issues that are causing test/failures currently. actions can be re-run but buildkite cannot so is being bypassed for now

@odunbar odunbar changed the title [WIP] Joss paper Joss paper Jan 9, 2024
author odunbar <odunbar@caltech.edu> 1698353497 -0700
committer odunbar <odunbar@caltech.edu> 1704479575 -0800

workflow to build joss paper

stub article

remove AAAS templating

added some intro and flushed out bib

sketch statement of need

remove $

start state-of-field

info

rm changes to test (wrong branch)

added ABC

more details for ABC and the examples

ORCIDs

expand bullets

expanded bullets

bib typo

language

NSF ack

fix format

MFH comments

MB comments

author list

ack

acknow

language for res. proj.

add sinusoid figs into paper

another fig

added more details

spacing

math format

format

language

code reformat, fig resize

reformat

language

code format

Added Laura affiliations and previous UQ paper and corrected a few tiny typos

lm2612 comments

added modules

typo

title typo...
@odunbar odunbar merged commit 5ada8e7 into main Jan 12, 2024
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