# Tag Info

Accepted

### Variational inference versus MCMC: when to choose one over the other?

For a long answer, see Blei, Kucukelbir and McAuliffe here. This short answer draws heavily therefrom. MCMC is asymptotically exact; VI is not. In the limit, MCMC will exactly approximate the target ...
• 8,734
Accepted

### When would one use Gibbs sampling instead of Metropolis-Hastings?

Firstly, let me note [somewhat pedantically] that There are several different kinds of MCMC algorithms: Metropolis-Hastings, Gibbs, importance/rejection sampling (related). importance and ...
• 104k
Accepted

### Hamiltonian Monte Carlo vs. Sequential Monte Carlo

Hamiltonian Monte Carlo performs well with continuous target distributions with "weird" shapes. It requires the target distribution to be differentiable as it basically uses the slope of the target ...
• 484
Accepted

### Posterior distribution and MCMC

If this was not a clear conflict of interest, I would suggest you invest more time on the topic of MCMC algorithm and read a whole book rather than a few (6?) articles that can only provide a partial ...
• 104k
Accepted

### Computation of the marginal likelihood from MCMC samples

The extension by Chib and Jeliazkov (2001) unfortunately gets quickly costly or highly variable, which is a reason why it is not much used outside Gibbs sampling cases. While there are many ways and ...
• 104k
Accepted

### Predictions from BSTS model (in R) are failing completely

Steve Scott here. I wrote the bsts package. I have a few suggestions for you. First, your seasonal components aren't doing what you think they are. I think you have daily data, because you're ...
• 326

• 1,809
Accepted

### Maximum likelihood parameters deviate from posterior distributions

With flat priors, the posterior is identical to the likelihood up to a constant. Thus MLE (estimated with an optimizer) should be identical to the MAP (maximum a posteriori value = multivariate mode ...
• 7,839

### Maximum likelihood parameters deviate from posterior distributions

Some possible generic explanations for this perceived discrepancy, assuming of course there is no issue with code or likelihood definition or MCMC implementation or number of MCMC iterations or ...
• 104k
Accepted

### What is the correct effective sample size (ESS) calculation?

After further research, I've made some useful discoveries. The answer appears to be anything but straightforward. Let me start by answering my second question above: "What is the correct effective ...
• 708
Accepted

### For Hamiltonian Monte Carlo, why does negating the momentum variables result in a symmetric proposal?

One of the reasons why the original construction of Hamiltonian Monte Carlo can be tricky to understand is that it is more restrictive than necessary, if only to simplify the theoretical proofs. In ...