Category: BayesianPage 4 of 7
support of binomial distribution $Binomial(N, p)$is {0,1,…, N}. In SBC, whose rank statistics are obtained by adding the number of smaller posterior samples out of L samples, the…
goal: structured additive regression models where the latent field is Gaussian, controlled by a few hyperparameters and with non‐Gaussian response variables {f(j)(·)}s are unknown functions of the covariates u,…
LPPD stands for log point-wise predictive density inference is based on each data point WAIC makes no assumption about the shape of the posterior (aic makes normality assumption…
We want to estimate intractable p(x) with tractable q(x) For approximation, Expectation propagation KL(p || q) Variational Bayesian minimize KL(q || p) VB generally requiring more iterations, however…
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]…