Prior Probability Calculator
Calculate or normalize prior probability values used in Bayesian inference.
Inputs
Comma-separated weights for each class/hypothesis
Highest Prior
0.5000
Most Likely Class
3
Number of Classes
3
Step by step
Sum of raw weights
3 + 2 + 5
= 10.0000
Normalized priors: each weight ÷ sum
[3, 2, 5] ÷ 10.00
= 0.3000, 0.2000, 0.5000
How it works
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.
Formula
Normalization
P(H_i) = weight_i / sum(all weights)
- weight_i
- Raw weight or count for hypothesis i
Frequently Asked Questions
How do you choose priors in practice?
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).
What if I have no prior knowledge?
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.