datapine

NOVEMBER 19, 2019

Social BI indicates the process of gathering, analyzing, publishing, and sharing data, reports, and information. One of the most imperative features of social BI is its ability to create self-served and user-generated analysis, coupled with the application of business user knowledge. What is Social Business Intelligence?

Business Intelligence 146

Business Intelligence Knowledge Discovery Dashboards Unstructured Data 146

Ontotext

MAY 18, 2020

These are the so-called supercomputers, led by a smart legion of researchers and practitioners in the fields of data-driven knowledge discovery. The capacity and performance of supercomputers is measured with the so-called FLOPS (floating point operations per second). What are supercomputers and why do we need them?

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Domino Data Lab

DECEMBER 4, 2020

The drug under investigation is an anti-inflammatory agent, and the study looks at self-reported pain relief and plasma concentration over time. The study features 4 arms (including the placebo arm), using doses of 5mg, 20mg, and 80mg, administered at time 0, and tracks the self-reported pain relief and drug concentration at 0, 0.5,

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

datapine

NOVEMBER 19, 2019

Social BI indicates the process of gathering, analyzing, publishing, and sharing data, reports, and information. One of the most imperative features of social BI is its ability to create self-served and user-generated analysis, coupled with the application of business user knowledge. What is Social Business Intelligence?

Business Intelligence 20

Business Intelligence Knowledge Discovery Unstructured Data Software 20

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. From a Bayesian perspective, one can combine joint posterior samples for $E[Y_i | T_i=t, E_i=j]$ and $P(E_i=j)$, which provides a measure of uncertainty around the estimate. Henne, and Dan Sommerfield. ACM, 2017. [4]

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Posteriors are useful to understand the system, measure accuracy, and make better decisions. Methods like the Poisson bootstrap can help us measure the variability of $t$, but don’t give us posteriors either, particularly since good high-dimensional estimators aren’t unbiased.

KDD 40

KDD Testing Machine Learning Modeling 40