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

O'Reilly on Data

JANUARY 22, 2019

All models, therefore, need to quantify the uncertainty inherent in their predictions. Yet, finance textbooks, programs, and professionals continue to use the normal distribution in their asset valuation and risk models because of its simplicity and analytical tractability. Let’s consider a specific example of interest rates.

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Cloudera

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In a world rife with uncertainty, governments need to ensure that their citizens’ health and well-being are taken care of even as they seek to keep their economies afloat. This resulted in staff spending more time on more complex tasks while also reducing human errors and security risks. Providing more value to citizens through data.

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Digital Transformation Data Governance Data-driven Uncertainty

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

Serving the Public Through Data

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