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Springer MaxEnt 37 Bayesian Inference - Mathematics & Statistics

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Description

This proceedings volume from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering brings together research from scholars across multiple disciplines who employ inductive statistics methods. The MaxEnt 2017 workshop, held in Sao Carlos, Brazil from July 9-14, 2017, focuses on the foundations of the Bayesian paradigm and its applications in science and engineering. The collection examines Bayesian methods in depth, explores their comparison to frequentist statistics approaches, and demonstrates appropriate applications across diverse scientific fields. Part of the Springer Proceedings in Mathematics & Statistics series (volume 239), this book serves researchers and practitioners seeking to advance their understanding of Bayesian inference techniques and their practical implementation in modern research.

Key Features

MaxEnt 37 proceedings capture cutting-edge research from international workshop participants in Bayesian inference and maximum entropy methods.

Springer publication ensures academic rigour with peer-reviewed content from scholars across multiple scientific and engineering disciplines.

Covers foundational aspects of Bayesian paradigm alongside comparisons with frequentist statistics to aid methodological decision-making.

Volume 239 in Springer Proceedings in Mathematics & Statistics series provides authoritative reference material for inductive statistics research.

Explores real-world applications of Bayesian inference methods across diverse fields including science, engineering and data analysis.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
10 January 2019
Listed Since
09 January 2019

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