The number of bits of evidence is log2(p1/(1−p1))−log2(p0/(1−p0)).
Basically, if you view the probability as a fraction, p/(p+q), then a positive bit of evidence doubles p, while a negative bit of evidence doubles q.
You can imagine the ruler: 3% 6% 11% 20% 33% 50% 67% 80% 89% 94% 97%. Each is one more bit than the last. For probabilities near 0, it is roughly counting doublings, for probabilities near 1, it is counting doublings of the complement.
The number of bits of evidence is log2(p1/(1−p1))−log2(p0/(1−p0)).
Basically, if you view the probability as a fraction, p/(p+q), then a positive bit of evidence doubles p, while a negative bit of evidence doubles q.
You can imagine the ruler: 3% 6% 11% 20% 33% 50% 67% 80% 89% 94% 97%. Each is one more bit than the last. For probabilities near 0, it is roughly counting doublings, for probabilities near 1, it is counting doublings of the complement.
Thanks! I hope to use this for raising my daughter. When teaching forecasting and bayesian reasoning.