O'Reilly on Data

JANUARY 22, 2019

All models, therefore, need to quantify the uncertainty inherent in their predictions. Yet, finance textbooks, programs, and professionals continue to use the normal distribution in their asset valuation and risk models because of its simplicity and analytical tractability. Let’s consider a specific example of interest rates.

Modeling 134

Modeling Finance Uncertainty Data mining 134

Cloudera

SEPTEMBER 29, 2021

In a world rife with uncertainty, governments need to ensure that their citizens’ health and well-being are taken care of even as they seek to keep their economies afloat. This resulted in staff spending more time on more complex tasks while also reducing human errors and security risks. Providing more value to citizens through data.

Digital Transformation 79

Digital Transformation Data Governance Data-driven Uncertainty 79

Cloudera

MARCH 2, 2018

Bridgespan Group estimated in 2015 that only 6% of nonprofits use data to drive improvements in their work. For example, applying machine learning to wind forecasting is expected to reduce uncertainty in wind energy production by more than 45% and will allow utilities to integrate wind more easily with traditional forms of power supply.

Data-driven 42

Data-driven Prescriptive Analytics Descriptive Analytics Machine Learning 42

The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. Risk and Robustness Our estimates $widehat{beta}$ of the "true'' coefficients $beta$ of our model (1) depend on the random data we observe in experiments, and they are therefore random or uncertain. It is a big picture approach, worthy of your consideration.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. 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. For example, imagine a fantasy football site is considering displaying advanced player statistics.

Experimentation 95

Experimentation Statistics Testing Strategy 95

O'Reilly on Data

DECEMBER 9, 2019

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. and an error term ??

Statistics 27

Statistics Uncertainty Risk Marketing 27

The Unofficial Google Data Science Blog

MAY 31, 2016

Further, there is the risk that the increased ad spend will be less productive due to diminishing returns (e.g., The 100 geos were randomly assigned to control and treatment groups, and a geo experiment test period was set up for February 16 – March 15, 2015, with the 6 previous weeks serving as the pre-period.

Advertising 42

Advertising Testing Sales Statistics 42