Continuous spike-and-slab prior Created 2026-10-06 Updated 2026-10-07
A continuous spike-and-slab prior mixes a narrow continuous probability distribution around zero with a much wider component. It permits practical effect selection while retaining a continuous posterior density. Its narrow component is not the exact-zero mass of a point-null mixture prior. A latent component indicator aids computation; a wide-component draw can still have small magnitude, so posterior effect thresholds and component membership answer different questions.
Integrating the positive component's normal distribution prior gives its prior predictive distribution:
The negative component gives . Let and denote those centred normal densities. The point-null mixture prior assigns component odds , so the posterior odds of the positive component are
Its posterior probability is . Evidence depends on , so large effects of either sign favour the positive component. This is the marginal distribution before observing , not the posterior predictive law for a second observation conditional on it.
Introduce independent component indicators , and auxiliary slab effects , independent of the indicators. Set . Then inactive genes have exactly zero effect, while active genes have the stipulated normal distribution. With supplied as known data and elicited positive beta shapes, one suitable WinBUGS model is
model {
q ~ dbeta(aq, bq)
for (i in 1:N) {
z[i] ~ dbern(q)
u[i] ~ dnorm(0, 1/V)
theta[i] <- z[i]*u[i]
y[i] ~ dnorm(theta[i], 1)
}
}
The normal arguments are precisions, not variances. Supply all observed y values, monitor q, theta and optionally z, and assess Markov chain Monte Carlo convergence diagnostics. The proper auxiliary prior remains defined for inactive genes, where is not informed by the likelihood. This represents the exact point-null mixture prior, rather than replacing its point mass by a narrow continuous spike. Conditional on the indicators, has beta distribution , providing a useful check on the update.