Remove Finance Remove Statistics Remove Testing Remove Uncertainty
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Generative AI that’s tailored for your business needs with watsonx.ai

IBM Big Data Hub

To provide even deeper domain expertise, the Granite family of models was trained on enterprise-relevant datasets from five domains: internet, academic, code, legal and finance, all scrutinized to root out objectionable content, and benchmarked against internal and external models. for clients and partners to work with.

Testing 91
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Variance and significance in large-scale online services

The Unofficial Google Data Science Blog

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. Here, day-of-week is a time-based confounder.

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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends. A single model may also not shed light on the uncertainty range we actually face.

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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.

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Tackling changed requirements with comprehensive modernization

BI-Survey

Overnight, the impact of uncertainty, dynamics and complexity on markets could no longer be ignored. Local events in an increasingly interconnected economy and uncertainties such as the climate crisis will continue to create high volatility and even chaos. Think beyond finance. Is our chosen technology still fit for purpose?