Calculate or normalize prior probability values used in Bayesian inference.
Prior probabilities represent initial beliefs about hypotheses before observing any data. This calculator normalizes raw weights into a valid probability distribution that sums to 1. In Bayesian ML, priors encode domain knowledge and regularize learning when data is scarce.
Normalization
P(H_i) = weight_i / sum(all weights)
Common choices include uniform priors (equal probability, no preference), informative priors (based on domain expertise or previous studies), and conjugate priors (mathematically convenient for the chosen likelihood).
Use a uniform (flat) prior, which assigns equal probability to all hypotheses. This is called an uninformative or diffuse prior and lets the data dominate the posterior.