Prior Probability Calculator
Calculate or normalize prior probability values used in Bayesian inference.
About Prior Probability Calculator
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
Worked example
With the default values below, Prior Probability Calculator returns Highest Prior of 0.5000. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Raw Prior Weights
- 3, 2, 5
Results
- Highest Prior
- 0.5000
- Most Likely Class
- 3
- Number of Classes
- 3
Step by step
- 1Sum of raw weights= 10.0000
- 2Normalized priors: each weight ÷ sum= 0.3000, 0.2000, 0.5000
Assumptions and limits
- The result is derived only from the single input listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
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.