Maximum entropy inference as a special case of conditionalization

Synthese ◽  
1985 ◽  
Vol 63 (1) ◽  
pp. 55-74 ◽  
Author(s):  
Brian Skyrms
Keyword(s):  
Maximum Entropy ◽  
Special Case ◽  
Maximum Entropy Inference

scholarly journals An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1015 ◽  
Author(s):  
Carles Bretó ◽  
Priscila Espinosa ◽  
Penélope Hernández ◽  
Jose M. Pavía
Keyword(s):  
Machine Learning ◽  
Gross Domestic Product ◽  
Maximum Entropy ◽  
Simulation Study ◽  
Learning Algorithm ◽  
Predictive Ability ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
Machine Learning Approach ◽  
Maximum Entropy Inference

This paper applies a Machine Learning approach with the aim of providing a single aggregated prediction from a set of individual predictions. Departing from the well-known maximum-entropy inference methodology, a new factor capturing the distance between the true and the estimated aggregated predictions presents a new problem. Algorithms such as ridge, lasso or elastic net help in finding a new methodology to tackle this issue. We carry out a simulation study to evaluate the performance of such a procedure and apply it in order to forecast and measure predictive ability using a dataset of predictions on Spanish gross domestic product.

The Evolution of Complex Systems

1988 ◽  
Vol 43 (1) ◽  
pp. 73-77
Author(s):  
G. L. Hofacker ◽  
R. D. Levine
Keyword(s):  
Complex Systems ◽  
Maximum Entropy ◽  
Transition Probabilities ◽  
Maximum Entropy Principle ◽  
Average Energy ◽  
Entropy Principle ◽  
Fundamental Equations ◽  
Special Case ◽  
Extremal Properties

Abstract A principle of evolution of highly complex systems is proposed. It is based on extremal properties of the information I (X, Y) characterizing two states X and Y with respect to each other, I(X, Y) = H(Y) -H(Y/X), where H(Y) is the entropy of state Y,H (Y/X) the entropy in state Y given the probability distribu­tion P(X) and transition probabilities P(Y/X).As I(X, Y) is maximal in P(Y) but minimal in P(Y/X), the extremal properties of I(X, Y) con­stitute a principle superior to the maximum entropy principle while containing the latter as a special case. The principle applies to complex systems evolving with time where fundamental equations are unknown or too difficult to solve. For the case of a system evolving from X to Y it is shown that the principle predicts a canonic distribution for a state Y with a fixed average energy .

scholarly journals Maximum-Entropy Inference with a Programmable Annealer

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Nicholas Chancellor ◽  
Szilard Szoke ◽  
Walter Vinci ◽  
Gabriel Aeppli ◽  
Paul A. Warburton
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Inference

Maximum Entropy

2020 ◽  
pp. 161-184
Author(s):  
John Harte
Keyword(s):  
Maximum Entropy ◽  
Rapid Change ◽  
Entropy Theory ◽  
State Variables ◽  
Total Abundance ◽  
Inference Procedure ◽  
Functional Forms ◽  
Natural Historian ◽  
Parameter Values ◽  
Maximum Entropy Inference

A major goal of ecology is to predict patterns and changes in the abundance, distribution, and energetics of individuals and species in ecosystems. The maximum entropy theory of ecology (METE) predicts the functional forms and parameter values describing the central metrics of macroecology, including the distribution of abundances over all the species, metabolic rates over all individuals, spatial aggregation of individuals within species, and the dependence of species diversity on areas of habitat. In METE, the maximum entropy inference procedure is implemented using the constraints imposed by a few macroscopic state variables, including the number of species, total abundance, and total metabolic rate in an ecological community. Although the theory adequately predicts pervasive empirical patterns in relatively static ecosystems, there is mounting evidence that in ecosystems in which the state variables are changing rapidly, many of the predictions of METE systematically fail. Here we discuss the underlying logic and predictions of the static theory and then describe progress toward achieving a dynamic theory (DynaMETE) of macroecology capable of describing ecosystems undergoing rapid change as a result of disturbance. An emphasis throughout is on the tension between, and reconciliation of, two legitimate perspectives on ecology: that of the natural historian who studies the uniqueness of every ecosystem and the theorist seeking unification and generality.

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