Bayesian Studio
Computing Conditional Probabilities
Beta-Binomial
Normal-Normal
Gamma-Poisson
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Info Gain
0.000
nats
Bayesian Update
Prior
Posterior
The Kullback-Leibler divergence measures how many "nats" of information we gained. High divergence indicates our evidence was highly surprising.
Samples: ON
Perspective
Classical Bayes
P(H|E)
=
P(E|H)
·
P(H)
P(E)
Bayesian Studio
v3.2.0
Adjust Parameters
Prior Belief ($H$)
Incoming Evidence ($E$)