Remove 2017 Remove Experimentation Remove Strategy Remove Uncertainty
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Crucially, it takes into account the uncertainty inherent in our experiments. It is also a sound strategy when experimenting with several parameters at the same time. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

by ALEXANDER WAKIM Ramp-up and multi-armed bandits (MAB) are common strategies in online controlled experiments (OCE). These strategies involve changing assignment weights during an experiment. The first is a strategy called ramp-up and is advised by many experts in the field [1].

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Havmor’s VP IT Dhaval Mankad on ‘melting’ hurdles with a scoop of digital innovation

CIO Business Intelligence

My prominent achievements include managing the intricate implementation of GST and its setup in L&T in 2017 and at Havmor, where I recently completed 5 years, the SAP S4 HANA Greenfield implementation. During the last decade, I have led digital transformation initiatives which added two lac hours of productivity for various organizations.

IT 89
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The trinity of errors in applying confidence intervals: An exploration using Statsmodels

O'Reilly on Data

Because of this trifecta of errors, we need dynamic models that quantify the uncertainty inherent in our financial estimates and predictions. Practitioners in all social sciences, especially financial economics, use confidence intervals to quantify the uncertainty in their estimates and predictions.