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Let $N$ be the number of updates, $T$ be the 1 / the thin rate (e.g., 10 = return 1 out of every 10 samples), $B$ be the number of burnin samples, and $S$ be the number of returned samples. The numbe …

The results are stored in sims.array; reading the documentation of the bugs function return value in the R2WinBUGS manual should get you the rest of the way there. … I am not sure why you are making the two different bugs calls, each with two chains. Perhaps you could clarify your purpose? …

Edit: Answer substantially rewritten in light of clarifying comments. You can do it in several ways; probably the simplest is to force $\sum_{j=1}^{NGroups} \beta_j = 0$ at every iteration of the sa …

The difference between the two approaches is that the former generates a credible interval for $N_i$ based on the posterior predictive distribution (or your approximation thereto), but the latter gene …

These priors are probably meant to represent a lack of information about the associated parameters. As such, their parameters are picked mostly out of thin air - the thinking being, I assume, that if …

Given that the $\tau_j$ are i.i.d. Gamma with parameters $\alpha, \lambda$ (where $f(x) \propto \lambda^{\alpha} x^{\alpha-1}\exp\{-\lambda x\}$, to define the parameterization of the Gamma), then $\s …

1) The probabilistic dnorm, dunif etc. functions are just describing the probability distribution which the variable on the left hand side (lhs) is assumed to have. If the variable is a parameter, th …

The likelihood function of a zero-inflated Poisson variate $x$ can be written as: $$\mathcal{L}(\mu, p) = [p+(1-p)e^{-\mu}]^{1(y_i=0)}[(1-p)e^{-\mu}\mu^{y_i}/y_i!]^{1(y_i>0)}$$ where $p$ is the prob …

What we'd like to do, for clarity, is adjust the observation by: y.true[i] <- y[i] - e[i] y.true[i] ~ dnorm(mu[i], prec[i]) which certainly won't work in JAGS, but may in BUGS (which I don't have). …

JAGS and BUGS allow you to invert a matrix numerically (sigma[1:K, 1:K] <- inverse(tau[,]) in WinBUGS), so you don't actually need a closed-form expression for the precision matrix. …