Why can't we just add up tiny chances over time?
Image: WolfgangRieger, Public domain, via Wikimedia Commons
Why can't we just add up tiny chances over time?
Imagine you're rolling a fair six-sided die repeatedly. Each roll is independent, and you're curious about the chance of rolling a six after many rolls.
As you keep rolling, the chance of rolling a six each time stays the same. But when you try to add up all those tiny chances over time, they can become incredibly small numbers that computers struggle to handle.
Example
After 100 rolls, the chance of rolling a six each time is 1/6. Adding up 100 tiny chances (1/6 each) gives you a tiny number that's hard to work with.
Remember this
Log-probabilities help us avoid tiny numbers that computers can't handle well, making calculations easier.
Text adapted from Wikipedia, licensed under CC BY-SA 4.0.
Likelihood function
Why can't we just guess probabilities correctly all the time?
Exponential distribution
Why do we sometimes need to predict rare events?
cross-entropy equals negative log-likelihood for classification
Why does knowing the wrong probability help us measure information loss?
Entropy H = -Σ p(x) log₂ p(x) measures average surprise in bits
How do we measure uncertainty in everyday decisions?
to use log-transform: when data is right-skewed or spans multiple orders of magnitude
Why can't we just add numbers to compare incomes?
log-loss / cross-entropy loss penalizes: confident wrong predictions more heavily
How wrong predictions hurt more than they're supposed to
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