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Probabilistic Machine Learning for Finance and Investing : A Primer to Generative AI with Python by Deepak K. Kanungo (2023, Trade Paperback)
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- Compralo SubitoProbabilistic Machine Learning for Finance and Investing: A Primer to Generative
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Informazioni su questo prodotto
Product Identifiers
PublisherO'reilly Media, Incorporated
ISBN-101492097675
ISBN-139781492097679
eBay Product ID (ePID)24058626858
Product Key Features
Number of Pages264 Pages
Publication NameProbabilistic Machine Learning for Finance and Investing : a Primer to Generative Ai with Python
LanguageEnglish
SubjectMachine Theory, Data Modeling & Design, Intelligence (Ai) & Semantics, Finance / General, Programming Languages / Python
Publication Year2023
TypeTextbook
AuthorDeepak K. Kanungo
Subject AreaComputers, Business & Economics
FormatTrade Paperback
Dimensions
Item Height0.6 in
Item Weight16.3 Oz
Item Length9.2 in
Item Width7.2 in
Additional Product Features
LCCN2023-279856
Dewey Edition23
IllustratedYes
Dewey Decimal332.60285/631
SynopsisThere 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.
LC Classification NumberHG4515.2.K36 2023
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