Reviewer:
Michael McKerns
the Uncertainty Quantification Foundation
Arcadia, California, USA
Area of Expertiese: It's uncertain.
General Evaluation
Quality of the approach: meets
Quality of the writing: meets
Quality of the figures/tables: meets
Specific Evaluation
Is the code made publicly available and does the article sufficiently describe how to access it?
Yes.
Does the article present the problem in an appropriate context?
Yes.
Is the content of the paper accessible to a computational scientist with no specific knowledge in the given field?
For as "math-y" as the topic is, the author makes it as accessible as possible.
Does the paper describe a well-formulated scientific or technical achievement?
Yes.
Are the technical and scientific decisions well-motivated and clearly explained?
Yes.
Are the code examples (if any) sound, clear, and well-written?
Yes.
Is the paper factually correct?
As far as I can tell.
Is the language and grammar of sufficient quality?
Yes. It's actually excellently written.
Are the conclusions justified?
Yes.
Is prior work properly and fully cited?
This could use some improvement. There are similar ideas that both exist in python code and have been published, but there are no references to included. For example, some the capacity presented exists in mystic (reviewer's shameless plug) as well as some Bayesian python codes, and in tools outside of Python. Also, there are mentions of CUDA, BLAS, LAPACK, MPI, and so on that should have references. The point being, this work was not done in a bubble.
Should any part of the article be shortened or expanded? Please explain.
No.
In your view, is the paper fit for publication in the conference proceedings?
This is one of the better scientific articles I've read in quite a while, not just scientific computing, but science in general. So, in a word, yes.
Reviewer: Michael McKerns the Uncertainty Quantification Foundation Arcadia, California, USA
Area of Expertiese: It's uncertain.
General Evaluation
Specific Evaluation
Is the code made publicly available and does the article sufficiently describe how to access it?
Does the article present the problem in an appropriate context?
Is the content of the paper accessible to a computational scientist with no specific knowledge in the given field?
Does the paper describe a well-formulated scientific or technical achievement?
Are the technical and scientific decisions well-motivated and clearly explained?
Are the code examples (if any) sound, clear, and well-written?
Is the paper factually correct?
Is the language and grammar of sufficient quality?
Are the conclusions justified?
Is prior work properly and fully cited?
Should any part of the article be shortened or expanded? Please explain.
In your view, is the paper fit for publication in the conference proceedings?