Alternative approach to maximum-entropy inference

1984 ◽  
Vol 30 (5) ◽  
pp. 2638-2644 ◽  
Author(s):  
Y. Tikochinsky ◽  
N. Z. Tishby ◽  
R. D. Levine
Keyword(s):  
Maximum Entropy ◽  
Alternative Approach ◽  
Maximum Entropy Inference

scholarly journals ON FILLING-IN MISSING CONDITIONAL PROBABILITIES IN CAUSAL NETWORKS

2005 ◽  
Vol 13 (03) ◽  
pp. 263-280 ◽  
Author(s):  
J. B. PARIS
Keyword(s):  
Maximum Entropy ◽  
Missing Values ◽  
Entropy Solution ◽  
Implicit Assumption ◽  
Conditional Probabilities ◽  
Centre Of Mass ◽  
Inference Process ◽  
Alternative Approach ◽  
Causal Networks ◽  
Maximum Entropy Inference

This paper considers the problem and appropriateness of filling-in missing conditional probabilities in causal networks by the use of maximum entropy. Results generalizing earlier work of Rhodes, Garside & Holmes are proved straightforwardly by the direct application of principles satisfied by the maximum entropy inference process under the assumed uniqueness of the maximum entropy solution. It is however demonstrated that the implicit assumption of uniqueness in the Rhodes, Garside & Holmes papers may fail even in the case of inverted trees. An alternative approach to filling in missing values using the limiting centre of mass inference process is then described which does not suffer this shortcoming, is trivially computationally feasible and arguably enjoys more justification in the context when the probabilities are objective (for example derived from frequencies) than by taking maximum entropy values.

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.

scholarly journals Information Entropy Approach for a Disorderless One-Dimensional Lattice

2020 ◽  
Vol 2 (1) ◽  
pp. 107-113
Author(s):  
Luis Arturo Juárez-Villegas ◽  
Moisés Martínez-Mares
Keyword(s):  
Maximum Entropy ◽  
Information Entropy ◽  
Information Criterion ◽  
System Size ◽  
Reflection Symmetry ◽  
Fixed Size ◽  
Dimensional Lattice ◽  
One Dimensional ◽  
Fixed Energy ◽  
Alternative Approach

Dimensionless conductance through a disorderless lattice is studied using an alternative approach. Usually, the conductance of an ordered lattice is studied at a fixed size, either finite or infinite if the crystalline limit is reached. Here, we propose one to consider the set of systems of all sizes from zero to infinite. As a consequence, we find that the conductance presents fluctuations, with respect to system size, at a fixed energy. At the band edge, these fluctuations are described by a statistical distribution satisfied by an ensemble of chaotic cavities with reflection symmetry, which also satisfies a maximum-entropy, or minimum-information, criterion.

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
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