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AI-Enabled Contingency Planning is More Accessible Than Ever

David Menninger's Analyst Perspectives

We live in a time of uncertainty, not unpredictability. Especially when a business finds itself on an undefined journey with an unclear destination whether caused by internal events or the world at large having plans to deal with a range of outcomes increases the odds of success. Or, at least enduring the least amount of damage.

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Transforming FSI in ASEAN with Cloud Analytics

CIO Business Intelligence

auxmoney began as a peer-to-peer lender in 2007, with the mission of improving access to credit and promoting financial inclusion. Much of this reluctance stems from the regulatory environment, arising from lengthy reviews and approvals processes, or even simple near-term regulatory uncertainty. .

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Adapting to Change: Finding Opportunity in Crucible Moments

Alation

Uncertainty reigns These days, planning a dinner out can be as complicated as planning a global business acquisition. But recognizing the opportunity in uncertainty is what separates the winners from the pack. Silicon Valley VC firm Sequoia Capital agrees, pointing to all the uncertainty and change in the world as proof.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

Circle of Friends was a social community built atop Facebook that launched in 2007. They might deal with uncertainty, but they're not random. They celebrated a bit, then went on to fix the next biggest problem in the business. Case Study 2: Circle of Friends.

Metrics 157
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Why model calibration matters and how to achieve it

The Unofficial Google Data Science Blog

bar{pi} (1 - bar{pi})$: This is the irreducible loss due to uncertainty. If calibration matters, our recommendation is to follow the paradigm proposed by Gneiting (2007) : pick the best performing model amongst models that are approximately calibrated, where "approximately calibrated" is discussed in the next section.

Modeling 122
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

Crucially, it takes into account the uncertainty inherent in our experiments. There is also uncertainty related to our modeling choices — did we select the correct polynomial embedding function $f(x)$, or is the true relationship better described by a different polynomial embedding?

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. From a Bayesian perspective, one can combine joint posterior samples for $E[Y_i | T_i=t, E_i=j]$ and $P(E_i=j)$, which provides a measure of uncertainty around the estimate. 2] Scott, Steven L. 2015): 37-45. [3]