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My question originated from Xi'an's suggestion to check integrability against the posterior in my nonlinear hierarchical model. I did not check it, but had possible infinity in mind and found out that one of my conditionals (which is inverse gamma distribution) have infinite variance. So that sampling resulted in chains often being far away at the tails.

So, this raised a question for me:

How to properly sample from distributions with infinite variances like inverse gamma distribution (with $ \alpha = 2 $) or Levy distribution or any other distribution with infinite variance? What does MCMC offer? I tried to search for papers dealing with such issues, but still no luck.

My question originated from Xi'an's suggestion to check integrability against the posterior in my nonlinear hierarchical model. I did not check it, but had possible infinity in mind and found out that one of my conditionals (which is inverse gamma distribution) have infinite variance. So that sampling resulted in chains often being far away at the tails.

So, this raised a question for me:

How to properly sample from distributions with infinite variances like inverse gamma distribution (with $ \alpha = 2 $) or Levy distribution or any other distribution with infinite variance? What does MCMC offer? I tried to search for papers dealing with such issues, but still no luck.

My question originated from Xi'an's suggestion to check integrability against the posterior posterior in my nonlinear hierarchical model. I did not check it, but had possible infinity in mind and found out that one of my conditionals (which is inverse gamma distribution) have infinite variance. So that sampling resulted in chains often being far away at the tails.

So, this raised a question for me:

How to properly sample from distributions with infinite variances like inverse gamma distribution (with $ \alpha = 2 $) or Levy distribution or any other distribution with infinite variance? What does MCMC offer? I tried to search for papers dealing with such issues, but still no luck.

My question originated from Xi'an's suggestion to check integrability against the posterior in my nonlinear hierarchical model. I did not check it, but had possible infinity in mind and found out that one of my conditionals (which is inverse gamma distribution) have infinite variance. So that sampling resulted in chains often being far away at the tails.

So, this raised a question for me:

How to properly sample from distributions with infinite variances like inverse gamma distribution (with $ \alpha = 2 $) or Levy distribution or any other distribution with infinite variance? What does MCMC offer? I tried to search for papers dealing with such issues, but still no luck.

My question originated from Xi'an's suggestion to check integrability against the posterior posterior in my nonlinear hierarchical model. I did not check it, but had possible infinity in mind and found out that one of my conditionals (which is inverse gamma distribution) have infinite variance. So that sampling resulted in chains often being far away at the tails.

So, this raised a question for me:

How to sample to properly sample from distributions with infinite variances like inverse gamma distribution (with $ \alpha = 2 $) or Levy distribution or any other distribution with infinite variance? What does MCMC offer? I tried to search for papers dealing with such issues, but still no luck.

My question originated from Xi'an's suggestion to check integrability against the posterior posterior in my nonlinear hierarchical model. I did not check it, but had possible infinity in mind and found out that one of my conditionals (which is inverse gamma distribution) have infinite variance. So that sampling resulted in chains often being far away at the tails.

So, this raised a question for me:

How to properly sample from distributions with infinite variances like inverse gamma distribution (with $ \alpha = 2 $) or Levy distribution or any other distribution with infinite variance? What does MCMC offer? I tried to search for papers dealing with such issues, but still no luck.