Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python (Paperback) (ISBN-13: 9781492097679)

There are several reasons why probabilistic machine learning represents the next-generation ML framework and technology for finance and investing. This generative ensemble learns continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, retrodiction, prediction, and counterfactual reasoning. Probabilistic ML also lets you systematically encode personal, empirical, and institutional knowledge into ML models.

Whether they're based on academic theories or ML strategies, all financial models are subject to modeling errors that can be mitigated but not eliminated. Probabilistic ML 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.

Unlike conventional AI, these systems are capable of warning us when their inferences and predictions are no longer useful in the current market environment. By moving away from flawed statistical methodologies and a restrictive conventional view of probability as a limiting frequency, youﾃ ﾃ?ﾂ[ ll move toward an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you how.

Author: Deepak K. Kanungo
ISBN-10: 1492097675
ISBN-13: 9781492097679
Publisher: O'Reilly Media
Language: English
Published: 09/19/2023
Pages: 264
Format: Paperback
Weight: 0.95lbs
Size: 9.10h x 6.90w x 0.70d