Forecasting with Maximum Entropy: The interface between physics, biology, economics and information theory
Forecasting with Maximum Entropy: The Interface Between Physics, Biology, Economics, and Information Theory is an innovative and cross-disciplinary exploration of how the principle of maximum entropy (MaxEnt) can be applied to model, analyze, and predict complex systems across diverse scientific domains. Written by leading experts in statistical mechanics, information theory, and systems modeling, this book reveals how the MaxEnt framework provides a unified approach to understanding uncertainty, inference, and dynamics in both natural and human-made systems.
This book bridges theoretical concepts and real-world applications, showing how maximum entropy methods — rooted in physics — can yield deep insights into biological evolution, ecological balance, economic fluctuations, and data-driven decision-making. By integrating mathematics, information science, and empirical research, it demonstrates how forecasting can move beyond traditional statistics to harness entropy as a universal measure of knowledge and uncertainty.
Key Features:
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Interdisciplinary Scope: Connects physics, biology, economics, and information theory through the unifying concept of entropy-based forecasting.
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Theoretical Foundations: Explains the principle of maximum entropy, Bayesian inference, and probabilistic modeling in a clear and rigorous manner.
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Predictive Applications: Showcases how MaxEnt models are used to forecast biological population dynamics, economic trends, ecological systems, and physical processes.
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Information-Theoretic Approach: Highlights how entropy links data, uncertainty, and prediction, offering a powerful framework for scientific reasoning.
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Case Studies and Examples: Includes real-world illustrations and mathematical formulations that demonstrate practical uses of MaxEnt in multidisciplinary contexts.
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Research-Oriented Insight: A valuable reference for scientists, data analysts, and researchers aiming to bridge physical and informational sciences.
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Readable yet Rigorous: Balances accessibility for newcomers with mathematical depth for advanced readers.
