القائمة
Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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| name | regression-models |
| description | Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and JAGS implementations. |
data {
int<lower=0> N;
int<lower=0> K;
matrix[N, K] X;
vector[N] y;
}
parameters {
real alpha;
vector[K] beta;
real<lower=0> sigma;
}
model {
alpha ~ normal(0, 10);
beta ~ normal(0, 5);
sigma ~ exponential(1);
y ~ normal(alpha + X * beta, sigma);
}
generated quantities {
array[N] real y_rep;
for (n in 1:N)
y_rep[n] = normal_rng(alpha + X[n] * beta, sigma);
}
model {
for (i in 1:N) {
y[i] ~ dnorm(mu[i], tau)
mu[i] <- alpha + inprod(X[i,], beta[])
}
alpha ~ dnorm(0, 0.001)
for (k in 1:K) { beta[k] ~ dnorm(0, 0.001) }
tau ~ dgamma(0.001, 0.001)
sigma <- 1/sqrt(tau)
}
data {
int<lower=0> N;
int<lower=0> K;
matrix[N, K] X;
array[N] int<lower=0,upper=1> y;
}
parameters {
real alpha;
vector[K] beta;
}
model {
alpha ~ normal(0, 2.5);
beta ~ normal(0, 2.5);
y ~ bernoulli_logit(alpha + X * beta);
}
model {
for (i in 1:N) {
y[i] ~ dbern(p[i])
logit(p[i]) <- alpha + inprod(X[i,], beta[])
}
alpha ~ dnorm(0, 0.4) # SD ≈ 1.58
for (k in 1:K) { beta[k] ~ dnorm(0, 0.4) }
}
model {
alpha ~ normal(0, 5);
beta ~ normal(0, 2.5);
y ~ poisson_log(alpha + X * beta);
}
model {
for (i in 1:N) {
y[i] ~ dpois(lambda[i])
log(lambda[i]) <- alpha + inprod(X[i,], beta[])
}
}
parameters {
real alpha;
vector[K] beta;
real<lower=0> phi; // Overdispersion
}
model {
phi ~ exponential(1);
y ~ neg_binomial_2_log(alpha + X * beta, phi);
}
parameters {
real alpha;
vector[K] beta;
real<lower=0> sigma;
real<lower=1> nu; // Degrees of freedom
}
model {
nu ~ gamma(2, 0.1); // Prior on df
y ~ student_t(nu, alpha + X * beta, sigma);
}
transformed data {
matrix[N, K] Q = qr_thin_Q(X) * sqrt(N - 1.0);
matrix[K, K] R = qr_thin_R(X) / sqrt(N - 1.0);
matrix[K, K] R_inv = inverse(R);
}
parameters {
vector[K] theta;
real<lower=0> sigma;
}
model {
y ~ normal(Q * theta, sigma);
}
generated quantities {
vector[K] beta = R_inv * theta;
}
| Parameter | Weakly Informative | Reference |
|---|---|---|
| Intercept | normal(0, 10) | Scale of outcome |
| Coefficients | normal(0, 2.5) | Gelman et al. |
| SD (sigma) | exponential(1) | Half-normal alternative |
| Logistic coef | normal(0, 2.5) | ~4 logit units = extreme |