Category: StanPage 2 of 3
Computing the posterior is our target. Expectation to get the marginal as we might not know the exact data distribution. Even if we do, computation is burdensome in…
motivation: variability . and heavy computation of point-wise-hyperparmeter SBC check approximate the whole procedure using sensitivity to alpha and may work
codes are in https://colab.research.google.com/drive/1ssFzcGmUi8xsPWuT3ZrIaA4ZC7Z_k2zE dir(test_prophet) : returns all functions within an module
it’s not exactly log p(theta, x). log [p(f(phi), x) |J_f| ] = log p(theta, x) + log det(J_f). The sampling in Stan is done on the unconstrained parameters,…
example models가 많다 스탠은 assume our users are applied statisticians PyMC3 assumes its users are Python programmers https://discourse.mc-stan.org/t/jonathan-sedar-hierarchical-bayesian-modelling-with-pymc3-and-pystan/3207
Knowing the contents of this chapter which introduce the concept of hierarchical model and its famous application, eight school model, itself is helpful enough! 5.1 Constructing a parameterized…
traceplot(estimated_model)하면 바로 traceplot그려짐 단순 vector[n_groups] mu와 orderde[n_groups] mu의 traceplot비교 log_exp_sum random.choices(population, weights, k=#sample) compiled_model <- stan_model(“a.stan”) 항상 setwd(getwd())로 시작하기 for(i in 1:N) { for(k in 1:n_groups) { contributions[k]…
https://github.com/rmcelreath/rethinking MAP estimation Hamiltonian Monte Carlo estimation Posterior prediction Multilevel model formulas Nice covariance priors Non-centered parameterization Semi-automated Bayesian imputation Gaussian…