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Peer-Review Record

Self-Adaptive Acceptance Rate-Driven Markov Chain Monte Carlo Method Applied to the Study of Magnetic Nanoparticles

Computation 2021, 9(11), 124; https://doi.org/10.3390/computation9110124
by Juan Camilo Zapata * and Johans Restrepo
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Computation 2021, 9(11), 124; https://doi.org/10.3390/computation9110124
Submission received: 9 September 2021 / Revised: 11 October 2021 / Accepted: 13 October 2021 / Published: 19 November 2021
(This article belongs to the Section Computational Engineering)

Round 1

Reviewer 1 Report

Please see the attached file.

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The manuscript proposes a Metropolis-type Monte Carlo algorithm to study the hysteretic properties of an ensemble of independent and non-interacting magnetic nanoparticles. This is an interesting topic that deserves attention. The paper is well referenced and structured.

Comments:

The authors need to provide more details about the proposed algorithm including but not limited to target and proposal distributions, autocorrelation plots and effective sample size. These types of Monte Carlo samplers are known for their highly correlated draws. What is the thin size? If the authors do not use thinning, what are the reasons for not using it?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors addresses adequately the requested comments by the reviewer.

The paper is recommended for publication.

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