Stan is a software for obtaining Bayesian inference using the No-U-Turn sampling algorithm. It's specially useful to handle models where Gibbs sampler have highly correlated posteriors and thus takes too long to archive convergence.
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Difficulties with a Bayesian formulation of a model for human timing data
The Wing-Kristofferson model is a simple model of the behavior of a human trying to drum out a steady beat (that is, trying to mimic a metronome). Let $y_i$ be the $i$th interval between two drum ...
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Does stan do predictive posteriors?
Does stan (in particular, rstan) have built-in facilities to generate predictive posterior distributions?
It's not hard to generate the distribution from the stan fit, but I'd rather not reinvent the ...
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76 views
Multilevel bayesian with AR1 correlation structure
How do I fit a bayesian multilevel model with with AR(1) correlation structure?
I am trying to teach myself bayesian modelling and I am wondering how you could specify a multilevel model with an ...
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Using STAN (related to BUGS/JAGS) to do linear regression with with ARMA(1,1) noise?
EDIT: I've modified my STAN code and it looks like I am getting numbers close to using R's arima. The original code, now moved to the end, was incorrect.
I've been ...
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How can I model a proportion with BUGS/JAGS/STAN?
I am trying to build a model where the response is a proportion (it is actually the share of votes a party gets in constituencies). Its distribution is not normal, so I decided to model it with a beta ...
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Parameters without defined priors in Stan
I've just started to learn to use Stan and rstan. Unless I've always been confused about how JAGS/BUGS worked, I thought you always had to define a prior ...
