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. In isolation, the $x_1$-system is optimal: changing $x_1$ and leaving the $x_2$ at 0 will decrease system performance.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Sisense

APRIL 9, 2019

Originally created in 2006, it’s one of the most popular open source BI tools. Between the language undergirding it and the power of its architecture, Hadoop has found a sizable following, tackling core BI tasks like statistical analytics and Big Data processing, including handling huge volumes of data from fleets of IoT sensors and more!

Analytics 75

Analytics Big Data Software IoT 75

The Unofficial Google Data Science Blog

APRIL 17, 2017

For us, demand for forecasts emerged from a determination to better understand business growth and health, more efficiently conduct day-to-day operations, and optimize longer-term resource planning and allocation decisions. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

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

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. Cambridge University Press, (2006). [2] Journal of the American Statistical Association 68.341 (1973): 117-130. [5] Journal of the American Statistical Association, Vol.

Modeling 40

Modeling Statistics Advertising Testing 40

Occam's Razor

MARCH 23, 2021

From 2006: Is Real-Time Analytics Really Relevant? ). In our in-flight optimization journey thus far, we have worked to identify signals that are believable, and identifying at which point they become believable (ex: statistically significant). You have the start of a fabulous in-flight optimization engine.

Analytics 133

Analytics Scorecard Strategy Testing 133

Domino Data Lab

JULY 1, 2021

statistical model-based techniques – Using Machine Learning we can streamline and simplify the process of building NER models, because this approach does not need a predefined exhaustive set of naming rules. The process of statistical learning can automatically extract said rules from a training dataset. The CRF model.

Modeling 111

Modeling Statistics Testing Metrics 111