# Ronald Fisher

In 1922 Ronald Fisher introduced maximum likelihood: given the data we saw, choose the parameter value that explains it best. Probability describes the data; likelihood scores the hypothesis.

For a volatility forecast f, the Gaussian likelihood of tomorrow’s return leads directly to the QLIKE loss, QLIKE(y, f) = y/f − ln(y/f) − 1, used to train and compare GARCH and AI volatility forecasts.

Canonical page: https://21ifm.com/great-minds/fisher

## Explore

- [Maximum likelihood: which model explains the clues?](https://21ifm.com/ml/maximum-likelihood)
- [Learning from forecasts in markets (ML016)](https://21ifm.com/ml/learning-from-forecasts-in-markets)
- [Volatility: the human-scale measure of risk (MF007)](https://21ifm.com/quant/mathematics/volatility-human-scale-risk)