scholarly journals Bayesian statistics meets sports: a comprehensive review

2019 ◽  
Vol 15 (4) ◽  
pp. 289-312
Author(s):  
Edgar Santos-Fernandez ◽  
Paul Wu ◽  
Kerrie L. Mengersen
Keyword(s):  
Bayesian Statistics ◽  
Bayesian Methods ◽  
Bayesian Computation ◽  
Sports Analytics ◽  
Software Packages ◽  
Model Complex ◽  
Comprehensive Survey ◽  
Number Of Publications ◽  
Combine Information ◽  
Sports Activities

AbstractBayesian methods are becoming increasingly popular in sports analytics. Identified advantages of the Bayesian approach include the ability to model complex problems, obtain probabilistic estimates and predictions that account for uncertainty, combine information sources and update learning as new data become available. The volume and variety of data produced in sports activities over recent years and the availability of software packages for Bayesian computation have contributed significantly to this growth. This comprehensive survey reviews and characterizes the latest advances in Bayesian statistics in sports, including methods and applications. We found that a large proportion of these articles focus on modeling/predicting the outcome of sports games and on the development of statistics that provides a better picture of athletes’ performance. We provide a description of some of the advances in basketball, football and baseball. We also summarise the sources of data used for the analysis and the most commonly used software for Bayesian computation. We found a similar number of publications between 2013 and 2018 as compared to those published in the three previous decades, which is an indication of the growing adoption rate of Bayesian methods in sports.

Bayesian Statistics in Sociology: Past, Present, and Future

2019 ◽  
Vol 45 (1) ◽  
pp. 47-68 ◽  
Author(s):  
Scott M. Lynch ◽  
Bryce Bartlett
Keyword(s):  
Social Science ◽  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Bayes Theorem ◽  
Widespread Application ◽  
Software Packages ◽  
The Future ◽  
Direct Use ◽  
The Bayesian Approach

Although Bayes’ theorem has been around for more than 250 years, widespread application of the Bayesian approach only began in statistics in 1990. By 2000, Bayesian statistics had made considerable headway into social science, but even now its direct use is rare in articles in top sociology journals, perhaps because of a lack of knowledge about the topic. In this review, we provide an overview of the key ideas and terminology of Bayesian statistics, and we discuss articles in the top journals that have used or developed Bayesian methods over the last decade. In this process, we elucidate some of the advantages of the Bayesian approach. We highlight that many sociologists are, in fact, using Bayesian methods, even if they do not realize it, because techniques deployed by popular software packages often involve Bayesian logic and/or computation. Finally, we conclude by briefly discussing the future of Bayesian statistics in sociology.

In praise of Ecumenical Bayes

2011 ◽  
Vol 34 (4) ◽  
pp. 206-207 ◽  
Author(s):  
Michael D. Lee
Keyword(s):  
Statistical Analysis ◽  
Bayesian Statistics ◽  
Bayesian Methods ◽  
Bayesian Models

AbstractJones & Love (J&L) should have given more attention to Agnostic uses of Bayesian methods for the statistical analysis of models and data. Reliance on the frequentist analysis of Bayesian models has retarded their development and prevented their full evaluation. The Ecumenical integration of Bayesian statistics to analyze Bayesian models offers a better way to test their inferential and predictive capabilities.

BAYESIAN ANALYSIS OF BIG DATA IN INSURANCE PREDICTIVE MODELING USING DISTRIBUTED COMPUTING

2017 ◽  
Vol 47 (3) ◽  
pp. 943-961 ◽  
Author(s):  
Yanwei Zhang
Keyword(s):  
Big Data ◽  
Distributed Computing ◽  
Predictive Modeling ◽  
Bayesian Methods ◽  
Distributed Algorithm ◽  
Bayesian Computation ◽  
Actuarial Science ◽  
Parallel Method ◽  
Bayesian Hierarchical ◽  
The Empirical Analysis

AbstractWhile Bayesian methods have attracted considerable interest in actuarial science, they are yet to be embraced in large-scaled insurance predictive modeling applications, due to inefficiencies of Bayesian estimation procedures. The paper presents an efficient method that parallelizes Bayesian computation using distributed computing on Apache Spark across a cluster of computers. The distributed algorithm dramatically boosts the speed of Bayesian computation and expands the scope of applicability of Bayesian methods in insurance modeling. The empirical analysis applies a Bayesian hierarchical Tweedie model to a big data of 13 million insurance claim records. The distributed algorithm achieves as much as 65 times performance gain over the non-parallel method in this application. The analysis demonstrates that Bayesian methods can be of great value to large-scaled insurance predictive modeling.

Null-hypothesis tests are not completely stupid, but Bayesian statistics are better

1998 ◽  
Vol 21 (2) ◽  
pp. 215-216 ◽  
Author(s):  
David Rindskopf
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Statistics ◽  
Statistical Methods ◽  
Bayesian Methods ◽  
Null Hypothesis ◽  
Hypothesis Tests ◽  
Null Hypothesis Testing ◽  
Preferred Solutions ◽  
The Way

Unfortunately, reading Chow's work is likely to leave the reader more confused than enlightened. My preferred solutions to the “controversy” about null- hypothesis testing are: (1) recognize that we really want to test the hypothesis that an effect is “small,” not null, and (2) use Bayesian methods, which are much more in keeping with the way humans naturally think than are classical statistical methods.

scholarly journals Accounting for uncertainty: an application of Bayesian methods to accruals models

2021 ◽  
Author(s):  
Matthias Breuer ◽  
Harm H. Schütt
Keyword(s):  
Earnings Management ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Model Uncertainty ◽  
Standard Approach ◽  
Estimation Methods ◽  
Accounting Research ◽  
Software Packages ◽  
Empirical Accounting Research ◽  
Compare And Contrast

AbstractWe provide an applied introduction to Bayesian estimation methods for empirical accounting research. To showcase the methods, we compare and contrast the estimation of accruals models via a Bayesian approach with the literature’s standard approach. The standard approach takes a given model of normal accruals for granted and neglects any uncertainty about the model and its parameters. By contrast, our Bayesian approach allows incorporating parameter and model uncertainty into the estimation of normal accruals. This approach can increase power and reduce false positives in tests for opportunistic earnings management as a result of better estimates of normal accruals and more robust inferences. We advocate the greater use of Bayesian methods in accounting research, especially since they can now be easily implemented in popular statistical software packages.

Bayesian Basics and the Scientific Hypothesis

2019 ◽  
pp. 143-160
Author(s):  
Bradley E. Alger
Keyword(s):  
Model Selection ◽  
Hypothesis Testing ◽  
Conceptual Framework ◽  
Bayesian Statistics ◽  
Bayesian Methods ◽  
Bayes Theorem ◽  
Testing Framework ◽  
Parameter Evaluation ◽  
Bayesian Procedures ◽  
Scientific Hypothesis

This chapter covers the basics of Bayesian statistics, emphasizing the conceptual framework for Bayes’ Theorem. It works through several iterations of the theorem to demonstrate how the same equation is applied in different circumstances, from constructing and updating models to parameter evaluation, to try to establish an intuitive feel for it. The chapter also covers the philosophical underpinnings of Bayesianism and compares them with the frequentist perspective described in Chapter 5. It addresses the question of whether Bayesians are inductivists. Finally, the chapter shows how the Bayesian procedures of model selection and comparison can be pressed into service to allow Bayesian methods to be used in hypothesis testing in essentially the same way that various p-tests are used in the frequentist hypothesis testing framework.

Bayesian Statistics in Archaeology

2018 ◽  
Vol 47 (1) ◽  
pp. 435-453 ◽  
Author(s):  
Erik Otárola-Castillo ◽  
Melissa G. Torquato
Keyword(s):  
Bayesian Inference ◽  
Bayesian Statistics ◽  
Bayesian Methods ◽  
Null Hypothesis ◽  
Significance Testing ◽  
Archaeological Research ◽  
Historical Overview ◽  
Spatial Analyses ◽  
Null Hypothesis Significance Testing ◽  
Statistical Framework

Null hypothesis significance testing (NHST) is the most common statistical framework used by scientists, including archaeologists. Owing to increasing dissatisfaction, however, Bayesian inference has become an alternative to these methods. In this article, we review the application of Bayesian statistics to archaeology. We begin with a simple example to demonstrate the differences in applying NHST and Bayesian inference to an archaeological problem. Next, we formally define NHST and Bayesian inference, provide a brief historical overview of their development, and discuss the advantages and limitations of each method. A review of Bayesian inference and archaeology follows, highlighting the applications of Bayesian methods to chronological, bioarchaeological, zooarchaeological, ceramic, lithic, and spatial analyses. We close by considering the future applications of Bayesian statistics to archaeological research.

scholarly journals Experience in the formation of power elements of an aircraft wing using topological optimization approaches

2020 ◽  
pp. 88-96
Author(s):  
Yaroslav Khrebtiievskyi ◽  
Vitaliy Sukhov ◽  
Yaroslav Kozei
Keyword(s):  
Optimization Methods ◽  
Topological Optimization ◽  
Design Stage ◽  
Aircraft Wing ◽  
Aircraft Structures ◽  
Software Packages ◽  
Number Of Publications ◽  
Technical Solutions ◽  
Quantitative Indicators ◽  
Intensive Search

The process of designing aircraft structures, in most cases, involves finding solutions that will minimize weight, while maintaining the characteristics of strength and rigidity. To increase the mass efficiency of the aircraft glider, it is proposed to carry out topological optimization of structural and power elements at the initial stages of design with the help of modern software packages. Modern wings of traditional forms are close to exhausting their aerodynamic and weight characteristics, so all over the world there is an intensive search for new technical solutions. This indicates the relevance of developing new methods that use high-precision mathematical modeling in the early stages of design. Despite the significant number of publications on the topic of mass optimization, including aircraft structures, there are no quantitative indicators of the magnitude of the possible minimization of mass by topological optimization methods for the main structural and power elements of the aircraft wing. The article describes the results of the analysis of the effectiveness of the use of topological optimization methods to minimize the mass of the main structural and power elements of the aircraft wing. For a typical, typesetting structural and power scheme of a light aircraft wing, the values of possible minimization of the glider mass were determined. The use of topological optimization at the design stage of the power elements of the aircraft glider makes it possible to significantly reduce the mass of the main structural and power elements and allows to significantly reduce the takeoff mass of the aircraft. This approach using the results of optimization can be used to determine rational power schemes and predict the mass of the wings, taking into account the peculiarities of their geometric shapes and boundary conditions.

scholarly journals Indices of Effect Existence and Significance in the Bayesian Framework

2019 ◽  
Author(s):  
Dominique Makowski ◽  
Mattan S. Ben-Shachar ◽  
SH Annabel Chen ◽  
Daniel Lüdecke
Keyword(s):  
Sample Size ◽  
Bayesian Statistics ◽  
Prior Knowledge ◽  
Bayesian Methods ◽  
Statistical Significance ◽  
Bayesian Framework ◽  
Current Debate ◽  
P Value ◽  
Psychological Science ◽  
Scientific Reporting

Turmoil has engulfed psychological science. Causes and consequences of the reproducibility crisis are in dispute. With the hope of addressing some of its aspects, Bayesian methods are gaining increasing attention in psychological science. Some of their advantages, as opposed to the frequentist framework, are the ability to describe parameters in probabilistic terms and explicitly incorporate prior knowledge about them into the model. These issues are crucial in particular regarding the current debate about statistical significance. Bayesian methods are not necessarily the only remedy against incorrect interpretations or wrong conclusions, but there is an increasing agreement that they are one of the keys to avoid such fallacies. Nevertheless, its flexible nature is its power and weakness, for there is no agreement about what indices of “significance” should be computed or reported. This lack of a consensual index or guidelines, such as the frequentist p-value, further contributes to the unnecessary opacity that many non-familiar readers perceive in Bayesian statistics. Thus, this study describes and compares several Bayesian indices, provide intuitive visual representation of their “behavior” in relationship with common sources of variance such as sample size, magnitude of effects and also frequentist significance. The results contribute to the development of an intuitive understanding of the values that researchers report, allowing to draw sensible recommendations for Bayesian statistics description, critical for the standardization of scientific reporting.

Bayesian statistics

2010 ◽  
pp. 477-497
Author(s):  
Janet L. Peacock ◽  
Philip J. Peacock
Keyword(s):  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Posterior Distributions ◽  
Bayesian Analyses ◽  
Prior Distributions ◽  
Unknown Quantity ◽  
Frequentist Approach ◽  
Pros And Cons ◽  
The Bayesian Approach

Bayesian statistics 478 How Bayesian methods work 480 Prior distributions 482 Likelihood; posterior distributions 484 Summarizing and presenting results 486 Using Bayesian analyses in medicine 488 Software for Bayesian statistics 492 Reading Bayesian analyses in papers 494 Bayesian methods: a summary 496 In this chapter we describe the Bayesian approach to statistical analysis in contrast to the frequentist approach. We describe how Bayesian methods work including a description of prior and posterior distributions. We outline the role and choice of prior distributions and how they are combined with the data collected to provide an updated estimate of the unknown quantity being studied. We include examples of the use of Bayesian methods in medicine, and discuss the pros and cons of the Bayesian approach compared with the frequentist approach Finally, we give guidance on how to read and interpret Bayesian analyses in the medical literature....

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