Bayesian Measurement error survival model

I’m want to fit a Weibull regression, but I’m considering a covariate to prone measurement error. In particular classical error model. However, I have questions about the priors, because, I have had some problems with the convergence.

This is my code.

data{
int n;
vector[n] x_obs; // measurement of x_true
vector[n] Time;
vector[n] x; // group
real<lower=0> tau; // measurement noise
int nbetaS;
vector[n] death;
int K;
vector[K] xk;
vector[K] wk;
}

parameters{
vector[n] x_true; // unknow true value
real<lower=0> taux; # prior scale
real mux;
vector[nbetaS] betaS;
real alpha;
real<lower=0> gamma;
}

model{

matrix[n,K] h;
vector[n] cumHaz;
// LOG-PRIORS

// Measurement error

target += gamma_lpdf(taux | 2, 5);
target += normal_lpdf(mux | 0, 1);
target += normal_lpdf(x_true| mux, taux);
target += normal_lpdf(x_obs| x_true, tau);
// Survival fixed effects
target += normal_lpdf(betaS | 0, 100);

// Association parameter
target += normal_lpdf(alpha | 0, 100);

// Weibull shape parameter
target += gamma_lpdf(gamma | 2, 1);

// LOG-LIKELIHOOD FOR SURVIVAL SUBMODEL
for(i in 1:n){
for(j in 1:K){
// hazard function
h[i,j] = gammapow(Time[i]/2(xk[j]+1), gamma-1) * exp( betaS[1] + betaS[2]x[i] + alphax_true[i] );
}

	// Cumulative hazard H[t] = int_0^t h[u] du - Gauss-Legendre quadrature
	cumHaz[i] = Time[i]/2*dot_product(wk,h[i,]);

	// Survival log-likelihood
	target += death[i]*log(h[i,K]) - cumHaz[i];
}

}

Thanks a lot!