Hello Community,

I have always been curious of why sometimes the chains tend to converge to a different value close to the end of the “warmup” phase. Sometimes do a substantial jump.

In this case, for example I have this

The chains seem to mix decently and suddently change behavious when the sampling time is approaching.

P.S. Although this is a general question, FYI the (work in progress) model is the following.

```
data{
int G; // Number of marker genes
int P; // Number of cell types
int S; // Number of mix samples
int<lower=1> R; // Number of covariates (e.g., treatments)
matrix[S,R] X; // Array of covariates
matrix<lower=0>[S,G] y; // Mix samples matrix
matrix<lower=0>[G,P] x;
// Background cell types
vector<lower=0, upper=1>[S] p_ancestor;
matrix[S,G] y_bg_hat;
}
transformed data{
matrix<lower=0>[S,G] y_log; // Mix samples matrix
y_log = log(y+1);
}
parameters {
simplex[P] beta[S]; // coefficients for predictors
real<lower=0> sigma; // error scale
matrix[R,P] alpha; // Prior to a
vector<lower=0.1>[P] phi;
}
transformed parameters{
matrix[S,P] beta_adj;
matrix[S,G] y_hat;
for(s in 1:S) beta_adj[s] = to_row_vector(beta[s]) * p_ancestor[s];
y_hat = beta_adj * x';
y_hat = y_hat + y_bg_hat;
}
model {
matrix[S,P] beta_hat;
// Regression
sigma ~ normal(0, 0.1);
// Dirichlet prior on proportions
alpha[1] ~ normal(0, 1);
if(R>1) to_vector(alpha[2:R]) ~ normal(0,1);
phi ~ normal(0.1, 5); #phi ~ normal(phi_prior[1], phi_prior[2]);
// Regression
to_vector(y_log) ~ student_t(8, log(to_vector(y_hat)), sigma);
// Hypothesis testing
beta_hat = X * alpha;
for(s in 1:S) for(p in 1:P) beta_hat[s,p] = inv_logit(beta_hat[s,p]);
for(s in 1:S) beta[s] * mean(p_ancestor) ~ beta(beta_hat[s] .* to_row_vector(phi), (1 - beta_hat[s]) .* to_row_vector(phi));
}
```