Stan Hamiltonian Monte Carlo, No-U-Turn Sampler C++
BUGS HMC
int real vector row_vector matrix
*1010.3 BUGS model! {! for (i in 1:N) {! Y[i] ~ dbin(q[i], 8)! logit(q[i]) # read data! > d ! 1010.3
> model
> data ! > fit print(fit, digits = 2)! Inference for Stan model: model.! 3 chains, each with iter=10100; warmup=100; thin=10; ! post-warmup draws per chain=1000, total post-warmup draws=3000.! ! mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat! beta 0.04 0.01 0.33 -0.60 -0.18 0.04 0.27 0.69 3000 1! s 3.04 0.01 0.37 2.41 2.78 3.02 3.27 3.80 2807 1! lp__ -443.66 0.18 9.50 -463.66 -449.84 -443.33 -437.14 -425.83 2805 1! ! Samples were drawn using NUTS(diag_e) at Tue Jun 17 15:45:43 2014.! For each parameter, n_eff is a crude measure of effective sample size,! and Rhat is the potential scale reduction factor on split chains (at ! convergence, Rhat=1).
> traceplot(fit)
OpenBUGS 1010.3 chain: 3 iteration: 10000 burn-in (warmup): 100 thin: 10 Mac Pro (2.8 GHz Quad-Core Intel Xeon) OS X 10.9.3 OpenBUGS 3.2.2 RStan/CmdStan 2.2.0
