# Tag Info

Accepted

### What is causing autocorrelation in MCMC sampler?

When using Markov chain Monte Carlo (MCMC) algorithms in Bayesian analysis, often the goal is to sample from the posterior distribution. We resort to MCMC when other independent sampling techniques ...

### Naive SE vs Time Series SE: which statistics should I report after Bayesian estimation?

These are measures of the computational MCMC error for the estimation of the posterior expected value of a parameter. One way of interpreting them is by comparing this MCMC error with the Standard ...
Accepted

### Multi-level Bayesian hierarchical regression using rjags

You want a distribution for each quarter (given a state), each state (given a region), and each region. That means you'll need at least some state parameters indexed by s (in your model b0, b1, ...

### Is it make sense to set the vague prior when your data size is small?

The use of vague or informative prior depends on the amount of knowledge that you have for the parameters that you want to assign the prior. I consider the following cases: No experts information and ...

### How to implement credible 95% interval for median odds ratio using JAGS?

I don't know if this is a solution for you, but since the lme4 glmer function can provide random intercept posterior median estimates and their conditional variance - and under the assumption of ...
Accepted

### My MCMC do not overlap : Mixturemodel with JAGS and R

Imagine you have a mixture of two normal distributions, the one on the left (L) and the one on the right (R) side of the plot presented below. To estimate $\mu_L$ and $\mu_R$ parameters you decide to ...

### Invalid parent values in JAGS

This is just a guess, but one of the distributions might be receiving invalid values. For example you have the line: ...
Accepted

### What prior distributions could/should be used for the variance in a hierarchical bayesisan model when the mean variance is of interest?

I disagree with the way you interpret Gelman concerning the choice of the Gamma for scale parameter. The basis of hierarchical modeling is to relate individual parameters to a common one through a ...

### Bayesian approach systematically overestimates sigma (SD)

I haven't checked everything in your zip file, but the problem seemed to be simple enough based on the JAGS model you have posted. The discrepancy between sd and JAGS output is due to sensitivity to ...
Accepted

### rjags mixture model for a combination of normal and gamma distributions

It is relatively easy to implement a mixture model where the different distributions have the same parametric family - the dnormmix distribution in JAGS does this using an inbuilt distribution for a ...
Accepted

### Bayesian autoregressive model with second peak at 1 in posterior distirbution of AR parameter

The peak can be eliminated by using a different prior for $\mu$. The simplest way to implement the new prior is to change the parameterization. Currently, you have \begin{equation} y_{t+1} = (1-\rho)\,...
Accepted

### Bayesian p-value in wrong direction using step function in JAGS / BUGS

The so-called 'Bayesian p-value' does not have the same interpretation as a true p-value: remember that you do not have a formal hypothesis test so there is no real concept of a 'probability of the ...

### R alternatives to JAGS/BUGS

Probably the most powerful Bayesian package presently available in R is the RStan package (which has a whole website here). ...
Accepted

Try: ...

### MCMC converging to a single value?

This is more a comment, but as I do not have enough reputation I might as well answer. From my limited experience with MCMC samplers, what I have observed is that the parameters tend to stay fixed ...

### Bayesian variable selection -- does it really work?

If you used log returns, then you made a slightly biasing error but if you used future value divided by present value then your likelihood is wrong. Actually, your likelihood is wrong in either case. ...