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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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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

For us, demand for forecasts emerged from a determination to better understand business growth and health, more efficiently conduct day-to-day operations, and optimize longer-term resource planning and allocation decisions. Quantification of forecast uncertainty via simulation-based prediction intervals.

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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. Cambridge University Press, (2006). [2] These predictive posterior distributions have many uses such as in multi-armed bandit problems. bandit problems).

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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. Also notice that while the state in this model is Markov (i.e. Chapman & Hall, 3rd edn. Gramacy, R. and Polson, N. Holmes, C.

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Data Science, Past & Future

Domino Data Lab

The problems down in the mature bucket, those are optimizations, they aren’t showstoppers. They learned about a lot of process that requires that you get rid of uncertainty. They’re being told they have to embrace uncertainty. Then in 2006, they told me to go look at a website and sign up for a thing.