This is my first post here but I have been a silent follower of this forum for a while now and I learnt a lot from many of the folks here. Since this is my first post, let me briefly introduce myself first. I am Mohamed Tarek, a scientist at PumasAI Inc., a company which develops a proprietary statistical software for pharmacometrics called Pumas that is free for academics. Before joining PumasAI, I was a developer on the Turing.jl team and a few of you may know me from there. My background is in mechanical engineering and computer science and I am a self-taught Bayesian who came into the Bayesian world from the open source software development side. I learnt almost everything I know about Bayesian inference from books, videos, documentation and papers produced by people on this forum and I have had my fair share of struggles getting NUTS to work well for some models that I worked with.
Together with my co-authors, I recently completed an educational manuscript titled “A Practitioner’s Guide to Bayesian Inference in Pharmacometrics using Pumas” and I am seeking some feedback and constructive criticism from both the experts and non-experts on this forum. In this paper, I attempted to give a somewhat comprehensive guide to doing Bayesian inference in pharmacometrics using Pumas the software. There is a tutorial-like section with code snippets and there is a more theoretical section in which I try to explain all of the main concepts that a user may need to know about to effectively use Bayesian inference in pharmacometrics. This is an arguably bold move given the many great resources out there already. But I wanted to write something more concise, accessible and comprehensive than anything I could find out there and I wanted it to be customised to pharmacometrics to have a common language with my target audience. In this theoretical section, I tried to focus on the intuition first and the mathematical details second, if at all. The target audience is people in the pharmacometrics space with no Bayesian background and who would like to get their feet wet with using MCMC in their work but they don’t understand how to use it or what it does, but more importantly they are not interested in understanding all of the details. The paper was largely inspired from the fantastic Bayesian workflow paper by Gelman et al and the Torsten paper by Margossian et al. so I would like to sincerely thank the authors of both these papers.
I hope this post is not an inappropriate way to seek feedback on my manuscript. In the paper, I also have a brief section on “Limitations of Other Software” in which I hope I didn’t step on too many toes but I am open to correcting any claims made there if anything I said was inaccurate. Anyways, I will leave you to it now and please let me know if you have any comments or questions. You can post comments here, or you can personally message me on Discourse or by email. My email is in the paper. I intend to iterate on this paper for the next few months until I am happy with it so whenever you get to it, feel free to contact me with your feedback.