CIO Business Intelligence

NOVEMBER 14, 2022

A DSS supports the management, operations, and planning levels of an organization in making better decisions by assessing the significance of uncertainties and the tradeoffs involved in making one decision over another. Types of decision support system In the book Decision Support Systems: Concepts and Resources for Managers , Daniel J.

Data mining 129

Data mining Data-driven Statistics OLAP 129

Cloudera

DECEMBER 3, 2021

Systems should be designed with bias, causality and uncertainty in mind. Uncertainty is a measure of our confidence in the predictions made by a system. We need to understand and provide the greatest human oversight on systems with the greatest levels of uncertainty. System Design. Human Judgement & Oversight.

Uncertainty 102

Uncertainty Measurement Metrics Risk 102

The Unofficial Google Data Science Blog

JANUARY 14, 2016

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.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Occam's Razor

APRIL 8, 2013

This thought was in my mind as I was reading Lean Analytics a new book by my friend Alistair Croll and his collaborator Benjamin Yoskovitz. They preserve almost all original intent, but if you read the book, or see the cycle elsewhere, please don''t be surprised to see a slightly different version. KPI: Property bookings.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification. Journal of Official Statistics 6.1 Disaggregation of the time series into subseries and reconciliation of the subseries forecasts. 1990): 3. [3]

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. Also, these surveys, these are mini books: if you want to grab them, they are free downloads. Tukey did this paper. It’s a great read.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

The Unofficial Google Data Science Blog

AUGUST 27, 2019

But importance sampling in statistics is a variance reduction technique to improve the inference of the rate of rare events, and it seems natural to apply it to our prevalence estimation problem. Unpublished book chapter on importance sampling. [2] Statistical Science. Statistics in Biopharmaceutical Research, 2010. [4]

Metrics 98

Metrics Statistics Uncertainty Optimization 98