A transformation of Bayesian statistics:Computation, prediction, and rationality

Lenhard, Johannes

doi:10.1016/j.shpsa.2022.01.017

Cited by 3 publications

(1 citation statement)

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“…TBF prediction of complex mechanical products is a typical small sample problem. There are two methods to solve the problem: one is mathematical methods suitable for data analysis of small samples, such as Bayesian and poor information method; The former can improve the evaluation accuracy of the field test with small sample by fusing the simulation prior information [34][35][36][37], but there is problem of how to scientifically determine the prior distribution before information fusion. The evaluation accuracy will even drop when the prior information is distorted.…”

Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based approach for product maintenance prediction with reliability information conversion

Zhang

Xue

Yan

et al. 2022

Auton. Intell. Syst.

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Predictive maintenance (PdM) cannot only avoid economic losses caused by improper maintenance but also maximize the operation reliability of product. It has become the core of operation management. As an important issue in PdM, the time between failures (TBF) prediction can realize early detection and maintenance of products. The reliability information is the main basis for TBF prediction. Therefore, the main purpose of this paper is to establish an intelligent TBF prediction model for complex mechanical products. The reliability information conversion method is used to solve the problems of reliability information collection difficulty, high collection cost and small data samples in the process of TBF prediction based on reliability information for complex mechanical products. The product reliability information is fully mined and enriched to obtain more reliable and accurate TBF prediction results. Firstly, the Fisher algorithm is employed to convert the reliability information to expand the sample, and the compatibility test is also discussed. Secondly, BP neural network is used to realize the final prediction of TBF, and PSO algorithm is used to optimize the initial weight and threshold of BP neural network to avoid falling into local extreme value and improve the convergence speed. Thirdly, the mean-absolute-percentage-error and the Coefficient of determination are selected to evaluate the performance of the proposed model and method. Finally, a case study of TBF prediction for a remanufactured CNC milling machine tool (XK6032-01) is studied in this paper, and the results show that the feasibility and superiority of the proposed TBF prediction method.

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Section: Literature Reviewmentioning

confidence: 99%

A machine learning-based approach for product maintenance prediction with reliability information conversion

Zhang

Xue

Yan

et al. 2022

Auton. Intell. Syst.

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Bayesianism from a philosophical perspective and its application to medicine

Williamson

2022

The International Journal of Biostatistics

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Bayesian philosophy and Bayesian statistics have diverged in recent years, because Bayesian philosophers have become more interested in philosophical problems other than the foundations of statistics and Bayesian statisticians have become less concerned with philosophical foundations. One way in which this divergence manifests itself is through the use of direct inference principles: Bayesian philosophers routinely advocate principles that require calibration of degrees of belief to available non-epistemic probabilities, while Bayesian statisticians rarely invoke such principles. As I explain, however, the standard Bayesian framework cannot coherently employ direct inference principles. Direct inference requires a shift towards a non-standard Bayesian framework, which further increases the gap between Bayesian philosophy and Bayesian statistics. This divergence does not preclude the application of Bayesian philosophical methods to real-world problems. Data consolidation is a key challenge for present-day systems medicine and other systems sciences. I show that data consolidation requires direct inference and that the non-standard Bayesian methods outlined here are well suited to this task.

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Bayesian analysis for social science research

Luque,

Sosa

2024

MAS

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In this manuscript, we discuss the substantial importance of Bayesian reasoning in Social Science research. Particularly, we focus on foundational elements to fit models under the Bayesian paradigm. We aim to offer a frame of reference for a broad audience, not necessarily with specialized knowledge in Bayesian statistics, yet having interest in incorporating this kind of methods in studying social phenomena. We illustrate Bayesian methods through case studies regarding political surveys, population dynamics, and standardized educational testing. Specifically, we provide technical details on specific topics such as conjugate and non-conjugate modeling, hierarchical modeling, Bayesian computation, goodness of fit, and model testing.

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A transformation of Bayesian statistics:Computation, prediction, and rationality

Cited by 3 publications

References 44 publications

A machine learning-based approach for product maintenance prediction with reliability information conversion

A machine learning-based approach for product maintenance prediction with reliability information conversion

Bayesianism from a philosophical perspective and its application to medicine

Bayesian analysis for social science research

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