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The trinity of errors in financial models: An introductory analysis using TensorFlow Probability

O'Reilly on Data

All models, therefore, need to quantify the uncertainty inherent in their predictions. Errors in analysis and forecasting may arise from any of the following modeling issues: using an inappropriate functional form, inputting inaccurate parameters, or failing to adapt to structural changes in the market.

Modeling 134
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Disrupt and Innovate in a Data-Driven World

Cloudera

Bridgespan Group estimated in 2015 that only 6% of nonprofits use data to drive improvements in their work. For example, applying machine learning to wind forecasting is expected to reduce uncertainty in wind energy production by more than 45% and will allow utilities to integrate wind more easily with traditional forms of power supply.

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Fitting Bayesian structural time series with the bsts R package

The Unofficial Google Data Science Blog

Forecasting (e.g. Time series data are having something of a moment in the tech blogs right now, with Facebook announcing their "Prophet" system for time series forecasting (Taylor and Letham 2017), and Google posting about its forecasting system in this blog (Tassone and Rohani 2017).

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5 Accounting Tips for BEPS Adoption

Jet Global

Prior to 2015, a number of MNEs pursued tax planning strategies that effectively transferred profits from higher-tax jurisdictions to lower-tax countries, thereby eroding the tax-bases of the higher-tax jurisdictions. BEPS represents a change in global taxation, but it isn’t the only change.

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Decision-Making in a Time of Crisis

O'Reilly on Data

But when making a decision under uncertainty about the future, two things dictate the outcome: (1) the quality of the decision and (2) chance. This essay is about how to take a more principled approach to making decisions under uncertainty and aims to provide certain conceptual and cognitive tools for how to do so, not what decisions to make.