Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python (Fifth Early Release)

Whether based on academic theories or Machine Learning strategies, all financial models are at the mercy of modeling errors that can be mitigated but not eliminated. Probabilistic ML technologies are based on a simple and intuitive definition of probability and the rigorous calculus of probability theory.
These systems treat uncertainties and errors of financial and investing systems as features, not bugs. And they quantify uncertainty generated from inexact inputs and outputs as probability distributions, not point estimates. This makes for realistic financial inferences and predictions that are useful for decision-making and risk management. These systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment.
Unlike conventional AI systems, probabilistic Machine Learning (ML) systems treat errors and uncertainties as features, not bugs. They quantify uncertainty generated from inexact model inputs and outputs as probability distributions, not point estimates. Most importantly, these systems are capable of forewarning us when their inferences and predictions are no longer useful in the current market environment. These ML systems provide realistic support for financial decision-making and risk management in the face of uncertainty and incomplete information.
Probabilistic ML is the next generation ML framework and technology for AI-powered financial and investing systems for many reasons. By moving away from flawed statistical methodologies (and a restrictive conventional view of probability as a limiting frequency), you'll move toward an intuitive view of probability as a mathematically rigorous statistical framework that quantifies uncertainty holistically and successfully. This book shows you how.