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Martin Modrák

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This is a second post in my series on taming divergences in Stan models, see the first post in the series for a general introduction. Also see guide to Stan warnings Standard caveat: I am not an expert on Stan, I consider myself just an advanced user who likes to explain things.

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Sometimes you are developing a model that has multiple variants: maybe you want to consider several different link functions somewhere deep in your model, or you want to switch between estimating a quantity and getting it as data or something completely different. In these cases, you might have wanted to use optional parameters and/or data that apply only to some variants of your model.

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I have just finished my submission to StanCon Helsinki 2018 which deals with my ongoing work on Bayesian modelling for gene regulation. I submitted late, because I procrastinated a lot on it and I recently have terrible time management in general. So we will se if it will even get considered. View the compiled submission or check the Genexpi-Stan GitHub repository, to view the source code.

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Although my time with the Stan language has been enjoyable, there is one thing that is not fun when modelling with Stan. And it is the dreaded warning message: There were X divergent transitions after warmup. Increasing adapt_delta above 0.8 may help.

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This is a neat trick I found on Tyler Morgan-Wall’s Twitter and is originally attributed to Joe Cheng. You can run any Shiny app without blocking the session. My helper function to run ShinyStan without blocking is below: launch_shinystan_nonblocking <- function(fit) { library(future) plan(multisession) future( launch_shinystan(fit) #You can replace this with any other Shiny app ) } Hope that helps!

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Not long ago, I came across a nice blogpost by Kahtryn Morrison called A gentle INLA tutorial. The blog was nice and helped me better appreciate INLA. But as a fan of the Stan probabilistic language, I felt that comparing INLA to JAGS is not really that relevant, as Stan should - at least in theory - be way faster and better than JAGS.

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So I recently started programming in Elm and also did some stuff in Rust. Honestly, it was mostly a hype-driven decision, but the journey was definitely worth it. I also noticed that although those two languages differ in their target audience and use cases, they made many similar design decisions. I think this is no coincidence.

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I love thinking about other people’s statistical problems! So get in touch if you need help with: Developing and debugging statistical models (particularly Stan and INLA) Exploratory data analysis Statistical graphics Small Inquiries If I have time, I will answer to small inquiries on Stan Discourse, tag me in as @martinmodrak to get my attention. You can check out list of my previous answers.

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This is a personal webpage/blog of Martin Modrák, an academic researcher in bioinformatics, currently at Institute of Microbiology of the Czech Academy of Sciences, laboratory of bioinformatics. I also do a bit of statistical consulting on other people’s projects. Get in touch! I also used to do research in AI for computer games. And I have a defunct software engineering blog.