The Art of Causal Conjecture ( Artificial Intelligence Series) 1st Edition by Glenn Shafer – Ebook PDF Instant Download/Delivery. 026219368X, 9780262193689
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Product details:
ISBN 10: 026219368X
ISBN 13: 9780262193689
Author: Glenn Shafer
The Art of Causal Conjecture ( Artificial Intelligence Series) 1st Edition:
In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy.
The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences—medicine, business, engineering, and artificial intelligence—must act on causal conjectures based on more limited knowledge. Shafer’s understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences.
Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.
The Art of Causal Conjecture ( Artificial Intelligence Series) 1st Edition Table of contents:
- Chapter 1: Introduction
- Chapter 2: Event Trees
- Chapter 3: Probability Trees
- Chapter 4: The Meaning of Probability
- Chapter 5: Independent Events
- Chapter 6: Events Tracking Events
- Chapter 7: Events as Signs of Events
- Chapter 8: Independent Variables
- Chapter 9: Variables Tracking Variables
- Chapter 10: Variables as Signs of Variables
- Chapter 11: An Abstract Theory of Event Trees
- Chapter 12: Martingale Trees
- Chapter 13: Refining
- Chapter 14: Principles of Causal Conjecture
- Chapter 15: Causal Models
- Chapter 16: Representing Probability Trees
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