entry for wiener diffusion model#934
Conversation
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Thanks, so much, @Franzi2114. If nobody else can get to this, I can review it some time over the next week. |
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Perfec,t thanks @bob-carpenter! I was also in contact with @WardBrian before I did the PR. There are still three open points that I didn't manage to incorporate:
Please let me know when there occur more points that I should change. |
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Hi @Franzi2114 -- I've just pushed a commit that addresses each of your comments, since they're mostly quarto-wrangling:
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Pinging @bob-carpenter again -- I think the notifications for this got lost while you were away |
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Thanks for pinging. I will review this week. |
bob-carpenter
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Hi, Franzi:
I'm really sorry this is so many comments. Most of it's to try to get consistency with the rest of our documentation's formatting.
The most important comment is about the upper bounds for parameters that are missing from the examples.
If you'd rather not make the fixes yourself, I think I can go through and make almost all of them other than places where I had genuine questions.
| Censoring sometimes includes the response (i.e., it is known that the reaction time in a trial fell outside the response window, but which response was given is unknown). One method that has been used to model such data has involved inferring the numbers of missing responses of either kind from the observed relative frequencies of the two responses. This approach has the problem that quite specifc assumptions on the missing data have to be made (namely, that the proportions of the two kinds of responses are the same for responses within and outside the response window). | ||
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| Here is a more principled approach that uses the cumulative distribution functions and their complements to provide the likelihood of censored data. As before, let $L$ be the left reaction time bound, and $U$ the right reaction time bound, and consider decision times without inter-trial variabilities for the sake of simplicity. It follows that the likelihood $p_l$ of observing a left-censored data point | ||
| is given by |
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This isn't quite the right usage of "likelihood". See:
https://statmodeling.stat.columbia.edu/2026/03/20/a-data-model-is-not-just-a-likelihood/
If we have a data-generating distribution
Specifically, this means we can't say "likelihood of data" as the likelihood function, in order to exist, has already fixed the data.
This is confused in the literature all over the place, but BDA makes it clear, as does Aki's post.
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Which formulation would you prefer here?
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Dear @bob-carpenter, I worked through all your comments and made several changes! Could you have another look at it? I also had some questions regarding your posts. I wrote them below your comments. I am afraid that I messed something up with the all.bib file. Can you fix this? |
Summary
This PR adds a user's guide entry for the wiener diffusion model. It is linked to the PRs for the seven parameter diffusion model PDF and CDF functions
stan-dev/math#2822
stan-dev/math#3042
Copyright and Licensing
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Franziska Henrich
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