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Jouni Helske
@jounihelske.bsky.social
Academy Research Fellow at INVEST (University of Turku), PI of CAUSALTIME project and partner in PREDLIFE consortium. Bayesian statistics, causal inference, state space and hidden Markov models, and computational statistics in general.
157 followers185 following28 posts
Reposted by Jouni Helske
AVavehtari.bsky.social

priorsense package for prior and likelihood sensitivity analysis is now on CRAN. It works with Stan (brms, rstan or cmdstanr) fits, or just posterior draws. See more in Noa Kallioinen's post at discourse.mc-stan.org/t/priorsense...#Bayes#MCMC#rstats

Priorsense 1.0 is now on CRAN
Priorsense 1.0 is now on CRAN

priorsense 1.0 has been released and is now available on CRAN. priorsense is an R package for efficiently checking whether the posterior is sensitivity to changes to the prior or likelihood (see the ...

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JHjounihelske.bsky.social

I will discuss Bayesian causal inference for panel data using R package dynamite in StanCon2024, held on 10-12 September at Oxford. #causalinference#bayes#statsmc-stan.org/events/stanc...docs.ropensci.org/dynamite/

StanCon 2024
StanCon 2024

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JHjounihelske.bsky.social

Another though: Depending on your situation, you could perhaps define this as a multivariate response consisting of three binomial variables so that you first estimate number of success in the first level, and the number of failures from that is the number of trials for second level and so on? 3/3

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JHjounihelske.bsky.social

But if you are working on software which allows constraints/priors on model parameters, you could use same predictors for each level but constraint some coefficients to zero. 2/

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JHjounihelske.bsky.social

Technically I think it would be possible, and we actually entertained adding this possibility in github.com/ropensci/dyn..., but eventually decided not to implement this as the interpretation of the model and defining the reference level would be tricky in general. 1/

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JHjounihelske.bsky.social

New paper by my PhD student Lauri Valkonen et al. on combining experimental and observational data for causal inference in pricing decisions: muse.jhu.edu/pub/56/artic...#stats#causalinference#bayes

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Reposted by Jouni Helske
HShrwsal.bsky.social

šŸŽ“ Join us at the Finnish Health Economics Seminar Series on Monday 3 pm for a presentation by Thang Dang from Norwegian Institute of Public Health with a title " Language Training, Refugeesā€™ Healthcare Integration, and the Next Generationā€™s Health". #HealthEconomics

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Reposted by Jouni Helske
AHandrew.heiss.phd

Bayesian statsky friends! A reviewer is complaining that we used a Bayesian model on a full population of data (a country-week panel from 2020ā€“mid-2021), and that Bayesian methods can't be used there. That's wrong, but is there a citation we can use to say it's fine to do this with IR panel data?

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Profile banner
JH
Jouni Helske
@jounihelske.bsky.social
Academy Research Fellow at INVEST (University of Turku), PI of CAUSALTIME project and partner in PREDLIFE consortium. Bayesian statistics, causal inference, state space and hidden Markov models, and computational statistics in general.
157 followers185 following28 posts