Hi, I have the following Stan-code data { int N; // number of observations int K; // dimension of observations vector[K] y[N]; // observations: vector[K] mu_x; vector[K] mu_y; } parameters { vector [K] sigma_x; vector [K] sigma_y; vector [K] rho; } model { for k in (1:K) { vector[2] mu = [mu_x[k], mu_y[k]]'; // cholesky decomposition of covariance matrix matrix[2, 2] L_Sigma = [[sigma_x[k], 0], [rho[k] * sigma_y[k], sigma_y[k] * sqrt(1 - rho[k] * rho[k])]]; sigma_x[k] ~ normal(0, 20); sigma_y[k] ~ normal(0, 20); y[k] ~ multi_normal(mu[k], L_Sigma); } After running this, I get the posteriors of sigma_x, sigma_y and rho. When new data arrives I want to update the model. I believe the model is suited fort this because of the presence of the multivariate normal distribution. My question is how does the new update-Stan code looks? Do I read the three variables one-by-one as vectors in the data-section? Is the below code the correct way to go? data { int N; // number of observations int K; // dimension of observations vector[K] y[N]; // observations: a list of K vectors vector[K] mu_x; vector[K] mu_y; vector[K] sigma_x; vector[K] sigma_y; vector[K] rho; } parameters { vector [K] sigma_x; vector [K] sigma_y; vector [K] rho; } model { for k in (1:K) { vector[2] mu = [mu_x[k], mu_y[k]]'; // cholesky decomposition of covariance matrix matrix[2, 2] L_Sigma = [[sigma_x[k], 0], [rho[k] * sigma_y[k], sigma_y[k] * sqrt(1 - rho[k] * rho[k])]]; sigma_x[k] ~ normal(0, 20); sigma_y[k] ~ normal(0, 20); y[k] ~ multi_normal(mu, L_Sigma); } Thanks in advance

Sequential inference of this kind is not directly possible in Stan. There are ad hoc ways to attempt it regardless, but see here for a thread on why this can be folly.

Also see a work in progress for nonparametric updating Non-parametric Bayesian updating by @robertgrant

Update model when new observations are available

Modeling

yizhang December 1, 2021, 4:28pm 4

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Update model when new observations are available

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