Book description
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.
Publisher resources
Table of contents
- Preface
- 1. The Need for Probabilistic Machine Learning
-
2. Analyzing and Quantifying Uncertainty
- The Monty Hall Problem
- Axioms of Probability
- Inverting Probabilities
- Simulating the Solution
- Meaning of Probability
- Risk Versus Uncertainty: A Useless Distinction
- The Trinity of Uncertainty
- The No Free Lunch Theorems
- Investing and the Problem of Induction
- The Problem of Induction, NFL Theorems, and Probabilistic Machine Learning
- Summary
- References
- 3. Quantifying Output Uncertainty with Monte Carlo Simulation
- 4. The Dangers of Conventional Statistical Methodologies
- 5. The Probabilistic Machine Learning Framework
- 6. The Dangers of Conventional AI Systems
- 7. Probabilistic Machine Learning with Generative Ensembles
- 8. Making Probabilistic Decisions with Generative Ensembles
- Index
- About the Author
Product information
- Title: Probabilistic Machine Learning for Finance and Investing
- Author(s):
- Release date: August 2023
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492097679
You might also like
book
Machine Learning for Finance
A guide to advances in machine learning for financial professionals, with working Python code Key Features …
book
Deep Learning for Finance
Deep learning is rapidly gaining momentum in the world of finance and trading. But for many …
book
Machine Learning for Algorithmic Trading - Second Edition
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, …
book
Machine Learning and Data Science Blueprints for Finance
Over the next few decades, machine learning and data science will transform the finance industry. With …