There are (econometric) models that call for MVN heterogeneity but truncation on at least 1 dimension is necessary. For instance, estimated income must not be lower than observed expenditures. Having read through most threads on MVN, truncated MVN, truncated multivariate distributions, … on stan’s …

_lp functions aren’t used much but this is exactly when they’re useful. See the SUG section 18.8 . You can access the log prob accumulator with this function type and return Beta. I haven’t tested below but hopefully it will work without much tinkering. /** * Truncated Multivariate Normal * …

Welcome to discourse! You might find this post useful: [image] How to truncate a multivariate normal distribution? (sidebar, how to access old stan mailing lists posts?) Modeling Hello! I am attempting to build a dynamic linear model where the coefficients are constrained …

Does make_stuff work in transformed parameters the same way it does in generated quantities? I’ll try. EDIT: Yes, it does work the same way. Nice. I created a minimal example based on that post. A simple MVN with 3 dimensions and a constraint on the last. Ocular inspection of marginal histogram lo…

OK, follow-up question here … How do I parallelize this? map_rect evaluates to a vector. Therefore, I need to split Jacobian and draw in make_stuff. This way each run of make_stuff_draw and make_stuff_Jacobian will return a vector. These would be put together into a single vector, and I have to c…

You only need to call it once. No need to actually construct Beta as the only thing that matters for the sampler is the log_prob calculation. Though I haven’t actually tested this, so please report back if you find issues. If I wanted Beta I’d construct it in the generated quantities block, which sh…

Those are some really nice implementations of truncated MVN functions. However, I am still confused about what transformation/generation to do where. I do need Beta not just as a generated quantity, I need it as a parameter. I should have clarified that. Let me provide some mock code to illustrate …

Ah, ok. _lp functions return the draw, update target and can be used in transformed parameters. Nice. In my mock example, I don’t need _lpdf and _rng, just _lp in the transformed parameters section. This looks much more familiar. Will test this, then use map_rect to speed things up. multi_normal_c…

~ multi_normal()T[lower,upper] impossible in stan

Modeling

jsocolar June 22, 2021, 2:32am 2

Welcome to discourse! You might find this post useful:

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~ multi_normal()T[lower,upper] impossible in stan

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