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2

Your results look reasonable given your model and your other assumptions. I can't speak to whether the model (and the assumptions) are themselves reasonable. I'm going to change the notation a bit because I like to use the "$p$" to denote a probability density (or mass) function. So I'll use $\theta$ as the probability of "success" ...

0

It would help if you posted a reproducible example to look at what your data looks like. I'm no expert either but will try to help. For what I can see without looking at an example, you have two problems. One with your prior and another in your model set-up. In the prior, the dimension of V within each structure depends on the number of response variables ...

7

The difference with the standard burn-in step in MCMC is that the later is usually done blindly, as a fixed fraction of the overall number of iterations, e.g., 20%. Here the burn-in or warm-up step is more actively looking for a reasonable starting point, that is, one that is compatible with the target density. The performance of the approach however depends ...

6

If you have only one single chain (or if you want all your chains to be completely independent), then this procedure is not different from classical burn-in. However, it can accelerate convergence if you allow your chains to interact. Start from a random position, and let all your chains run independently for $T$ steps. Then, set all the walkers of all the ...

1

From the course notes for MCMCglmm section 1.2 describing prior distributions. For a single variance component the inverse Wishart takes two scalar parameters, V and nu. The distribution tends to a point mass on V as the degree of belief parameter, nu goes to infinity. The distribution tends to be right skewed when nu is not very large, with a mode of \$\...

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I haven't (and won't) checked what I'm saying in Stan (I haven't time to spend on compilation today!), so please try this out and let us know what happens. First, I'm pretty sure you're right that the issue is label switching. You should plot the traceplots (traceplot(my_stan_fit)) to confirm this. Basically, on some chains, alpha[1] and beta[1] belong to ...

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