Remove 2016 Remove Optimization Remove Uncertainty Remove Visualization
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Belcorp reimagines R&D with AI

CIO Business Intelligence

These circumstances have induced uncertainty across our entire business value chain,” says Venkat Gopalan, chief digital, data and technology officer, Belcorp. “As He points to cost savings from the reduction in laboratory tests, formulations, external software licenses, and the optimization of activities.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Crucially, it takes into account the uncertainty inherent in our experiments.

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6 ecommerce trends to watch

IBM Big Data Hub

Digital optimization and automation tools have made it cheaper and easier for businesses to use customer data or third-party data, creating intelligent ecommerce sites. a new living room couch—consumers can reduce uncertainty and the likelihood of returning a product by “trying it out” in their living room.

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Topics to watch at the Strata Data Conference in New York 2019

O'Reilly on Data

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. It’s up two places from 2017 and up six places from 2016. (A

IoT 20
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Fitting Bayesian structural time series with the bsts R package

The Unofficial Google Data Science Blog

If both variances are positive then the optimal estimator of $y_{t+1}$ winds up being "exponential smoothing," where past data are forgotten at an exponential rate determined by the ratio of the two variances. There are also plotting functions that you can use to visualize the regression coefficients. Compare to Figure 2.

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution. Often our data can be stored or visualized as a table like the one shown below. arXiv preprint arXiv:1602.00047, (2016). [8] bandit problems).