The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. They may contain parameters in the statistical sense, but often they simply contain strategically placed 0's and 1's indicating which bits of $alpha_t$ are relevant for a particular computation. by STEVEN L.

Forecasting 66

Forecasting Statistics Modeling Software 66

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. OTexts, 2014. Journal of Official Statistics 6.1 2014): 276. [7] References [1] Hyndman, Rob J., and George Athanasopoulos.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

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.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. CoRR, 2014. [2] Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew.

IT 70

IT Visualization Modeling Uncertainty 70