The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Crucially, it takes into account the uncertainty inherent in our experiments. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. We forecast this time series from the middle of 2006 through the end of the data, for a 30-month forecast horizon.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

The Unofficial Google Data Science Blog

MARCH 31, 2016

We often use statistical models to summarize the variation in our data, and random effects models are well suited for this — they are a form of ANOVA after all. In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution.

Modeling 40

Modeling Statistics Advertising Testing 40