I’m using brms for regression analysis assuming negative binomial model,
but I get " E-BFMI less than .2" error.
I read somewhere else that this error might be avoided by specifying appropriate priors,
but not sure how to improve my prior.

It’s difficult to comment on your priors without knowing more about your data and your model. But at a superficial glance, your intercept prior is pretty vague. Try something more like prior(normal(log(x), 1), class = Intercept), where the value of x is where you’d expect the mean of your data to be when all the predictors are at zero. Also, unless your predictors have extreme scales, I’d also reign in your class = b prior to something more like prior(normal(0, 1), class = b).

Thank you very much, seems like problem solved about the e-bfmi issue following your advice.
Still got issue with discrepancy between waic and loo(and loo’s pareto_k > 0.7 warnings), but I’ll create another thread for this issue if I cannot find any solution.

By the way, I’m modelling generalized linear model-like regression , with count data as response variable and several other explanatory variables, with autocorrelated errors.