The Data Visualisation Catalogue
DECEMBER 13, 2021
A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention. A Model of Perceptual Task Effort for Bar Charts and its Role in Recognizing Intention. User Modeling and User-Adapted Interaction , 16(1), 1–30. Journal of Experimental Psychology: Applied, 4 (2), 119–138. Bar charts and box plots.
Smart Data Collective
FEBRUARY 4, 2020
Current R&D Models Provide Diminishing Returns. In a report on the failure rates of drug discovery efforts between 2013 and 2015, Richard K. Now, picture the same process using heuristic models, machine vision, and artificial intelligence. Artificial intelligence can help us take better care of those we’ve left behind.
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The Unofficial Google Data Science Blog
APRIL 23, 2024
Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.
The Unofficial Google Data Science Blog
OCTOBER 7, 2015
by HENNING HOHNHOLD, DEIRDRE O'BRIEN, and DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling.
Hurwitz & Associates
JULY 19, 2018
The strategy evolved from earlier corporate moves to streamline HPE’s business, following the split of the traditional HP business in 2015, creating an HP business focused on PCs and printers – and HPE, focused on enterprise infrastructure. Consumption models are changing. Key Takeaways.
The Unofficial Google Data Science Blog
JULY 22, 2020
Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. When there are changing assignment weights and time-based confounders, this complication must be considered either in the analysis or the experimental design.
The Unofficial Google Data Science Blog
JUNE 30, 2016
In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. The choice of space $cal F$ (sometimes called the model ) and loss function $L$ explicitly defines the estimation problem. This is often referred to as the positivity assumption.
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