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What causes a high autocorrelation sample in MCMC?
High autocorrelation samples [from MCMC] often are caused by strong correlations among variables. I’m wondering what are other causes of high autocorrelation samples in MCMC.
What causes high autocorrelation in a Bayesian analysis?
When running a Bayesian analysis, one thing to check is the autocorrelation of the MCMC samples. But I don’t understand what is causing this autocorrelation. High autocorrelation samples [from MCMC] often are caused by strong correlations among variables.
Is there a tutorial for mcmcglmm in R?
MCMCglmm is a package for fitting Bayesian mixed models in R and was written by Jarrod Hadfield. Its use is roughly similar to lme4’s but there are some additional complexities that the user has to deal with. This tutorial aims to get you started with MCMCglmm and shows how the Bayesian analogue of an lme4 model can be implemented with MCMCglmm.
Which is an example of a mcmcglmm model?
For example, MCMCglmm supports multi-variate dependent variables and a wide variety of distributions, for instance, multinominal, exponential, zero-inflated, and censored distributions. Further, MCMCglmm can be used to implement Hurdle models and models with heterogenous residual variances.
How is autocorrelation related to the time scale?
On this larger time scale it appears that we have converged. One major difference between the two chains we’ve looked at is the level of autocorrelation in each. Autocorrelation is a number between negative 1 and positive 1 which measures how linearly dependent the current value of the chain is to past values called lags.
What is the correlation of autocorrelation in Monte Carlo?
A correlation of 1. With the first lag, a value of the chain has a correlation a little higher than 0.5. And as we go further along the chain, the values become less correlated. We can also look at the values of the auto correlation themselves with autocorr.diag. We run that and we look at those values.