Probabilistic Machine Learning for Finance and Investing is popular PDF and ePub book, written by Deepak K. Kanungo in 2023-08-14, it is a fantastic choice for those who relish reading online the Computers genre. Let's immerse ourselves in this engaging Computers book by exploring the summary and details provided below. Remember, Probabilistic Machine Learning for Finance and Investing can be Read Online from any device for your convenience.

Probabilistic Machine Learning for Finance and Investing Book PDF Summary

Whether based on academic theories or discovered empirically by humans and machines, 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. 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. They are generative ensembles that learn continually from small and noisy financial datasets while seamlessly enabling probabilistic inference, prediction and counterfactual reasoning. By moving away from flawed statistical methodologies (and a restrictive conventional view of probability as a limiting frequency), you can embrace an intuitive view of probability as logic within an axiomatic statistical framework that comprehensively and successfully quantifies uncertainty. This book shows you why and how to make that transition.

Detail Book of Probabilistic Machine Learning for Finance and Investing PDF

Probabilistic Machine Learning for Finance and Investing
  • Author : Deepak K. Kanungo
  • Release : 14 August 2023
  • Publisher : "O'Reilly Media, Inc."
  • ISBN : 9781492097631
  • Genre : Computers
  • Total Page : 287 pages
  • Language : English
  • PDF File Size : 11,8 Mb

If you're still pondering over how to secure a PDF or EPUB version of the book Probabilistic Machine Learning for Finance and Investing by Deepak K. Kanungo, don't worry! All you have to do is click the 'Get Book' buttons below to kick off your Download or Read Online journey. Just a friendly reminder: we don't upload or host the files ourselves.

Get Book

Machine Learning in Finance

Machine Learning in Finance Author : Matthew F. Dixon,Igor Halperin,Paul Bilokon
Publisher : Springer Nature
File Size : 17,8 Mb
Get Book
This book introduces machine learning methods in finance. It presents a unified treatment of machine...

Artificial Intelligence in Finance

Artificial Intelligence in Finance Author : Yves Hilpisch
Publisher : "O'Reilly Media, Inc."
File Size : 13,9 Mb
Get Book
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once th...

Artificial Intelligence in Asset Management

Artificial Intelligence in Asset Management Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
File Size : 54,9 Mb
Get Book
Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the se...

Probabilistic Machine Learning

Probabilistic Machine Learning Author : Kevin P. Murphy
Publisher : MIT Press
File Size : 25,6 Mb
Get Book
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of p...

Machine Learning

Machine Learning Author : Kevin P. Murphy
Publisher : MIT Press
File Size : 46,8 Mb
Get Book
A comprehensive introduction to machine learning that uses probabilistic models and inference as a u...