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Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

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Marque : GENERIC
Vendu par HEAVENBOOKS.MA

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. Probabilisti...

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Description produit
Marque
GENERIC
Titre principal
Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python
Editeur
O'Reilly Media
Type de produit
Paperback
Présentation du livre
Paperback
Release date
9/19/2023 12:00:00 AM
Langue d'origine
English
ISBN
1492097675
Dimensions
6.75 x 0.75 x 9 inches
Nombre de pages de livre
264 pages
Langue - Librairie
English
Résumé
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'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. Read more
Auteur(s)
Deepak K. Kanungo
Date de parution
9/19/2023 12:00:00 AM