Reasoning Under Uncertainty: Do the right thing!

The amount of digital data in the new era has grown exponentially in recent years and with the development of new technologies, is growing more rapidly than ever before. Simply recording data is one thing, whereas the ability to utilize it and turn it into a profit is another. Supposing we want to collect as many pieces of information as we can gather from any source, our database will be populated with a lot of sparse, unstructured, and not-explicitly-well-clear correlated data. In this essay we summarized the approach proposed in Chapter IV “Uncertain Knowledge and Representation” of the book “Artificial Intelligence: A Modern Approach” written by Russel S. and Norvig P., showing how the problem of reasoning under uncertainty is applied in data science, and in particular in the recent data revolution scenario. The proposed approach analyzes an extension of the Bayesian networks called Decisions networks that resulted to be a simple but elegant model for reasoning in presence of uncertainty.

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About Gianmario

Data Scientist with experience on building data-driven solutions and analytics for real business problems. His main focus is on scaling machine learning algorithms over distributed systems. Co-author of the Agile Manifesto for Data Science (datasciencemanifesto.com), he loves evangelising his passion for best practices and effective methodologies amongst the data geeks community.
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