A Foundational Approach to Generalising the Maximum Entropy Inference Process to the Multi-Agent Context

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.

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Maximum entropy inference of seabed attenuation parameters using ship radiated broadband noise

The Journal of the Acoustical Society of America ◽

10.1121/1.4936907 ◽

2015 ◽

Vol 138 (6) ◽

pp. 3563-3575 ◽

Cited By ~ 4

Author(s):

D. P. Knobles

Keyword(s):

Maximum Entropy ◽

Broadband Noise ◽

Maximum Entropy Inference

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An Entropy-Based Machine Learning Algorithm for Combining Macroeconomic Forecasts

Entropy ◽

10.3390/e21101015 ◽

2019 ◽

Vol 21 (10) ◽

pp. 1015 ◽

Cited By ~ 3

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.

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