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.

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....

scholarly journals Bayesian probabilities for occurrence of large earthquakes in the seismogenic sources of Japan and Phillipine during the period 1998-2017

2001 ◽  
Vol 34 (4) ◽  
pp. 1619
Author(s):  
T. M. TSAPANOS ◽  
O. CH. GALANIS ◽  
S. D. MAVRIDOU ◽  
M. P. HELMl
Keyword(s):  
Seismic Hazard ◽  
Bayesian Statistics ◽  
Poisson Distribution ◽  
Bayesian Approach ◽  
Seismic Sources ◽  
Time Interval ◽  
Seismogenic Sources ◽  
The One ◽  
Large Earthquakes ◽  
The Bayesian Approach

The Bayesian statistics is adopted in 11 seismic sources of Japan and 14 of Philippine in order to estimate the probabilities of occurrence of large future earthquakes, assuming that earthquakes occurrence follows the Poisson distribution. The Bayesian approach applied represents the probability that a certain cut-off magnitude (or larger) will exceed in a given time interval of 20 years, that is 1998-2017. This cut-off magnitude is chosen the one with M=7.0 or greater. In this case we can consider these obtained probabilities as a seismic hazard presentation. More over curves are produced which present the fluctuation of the seismic hazard between these seismic sources. These graphs of varying probability are useful either for engineering or other practical purposes

BAYESIAN OCKHAM’S RAZOR AND NESTED MODELS

2019 ◽  
Vol 35 (02) ◽  
pp. 321-338
Author(s):  
Bengt Autzen
Keyword(s):  
Bayesian Approach ◽  
Bayesian Methods ◽  
Economic Models ◽  
Philosophical Literature ◽  
Ockham’S Razor ◽  
Nested Models ◽  
Scientific Inference ◽  
Complex Hypothesis ◽  
Ockham's Razor ◽  
The Bayesian Approach

Abstract:While Bayesian methods are widely used in economics and finance, the foundations of this approach remain controversial. In the contemporary statistical literature Bayesian Ockham’s razor refers to the observation that the Bayesian approach to scientific inference will automatically assign greater likelihood to a simpler hypothesis if the data are compatible with both a simpler and a more complex hypothesis. In this paper I will discuss a problem that results when Bayesian Ockham’s razor is applied to nested economic models. I will argue that previous responses to the problem found in the philosophical literature are unsatisfactory and develop a novel reply to the problem.

Bayesian Statistics and Political Recruitment: A Comment

1976 ◽  
Vol 6 (1) ◽  
pp. 124-125
Author(s):  
Paul Whiteley
Keyword(s):  
Bayesian Statistics ◽  
Bayesian Approach ◽  
Subjective Probability ◽  
Analytical Techniques ◽  
Political Activism ◽  
Background Characteristics ◽  
Political Recruitment ◽  
Political Activist ◽  
The Individual ◽  
The Bayesian Approach

In an important contribution to the improvement of data analytical techniques in political science, Budge and Farlie examine the predictive success of various background characteristics in determining political activism [Ian Budge and Dennis Farlie, ‘Political Recruitment and Dropout’, this Journal, v (1975), 33–68]. The authors use the framework of Bayesian statistics, in which the subjective probability that a given individual will be a political activist is revised in the light of sample information about the background characteristics of activists to give a posterior (i.e. after the information or event) probability that the individual is an activist. Unfortunately, as the authors admit, they do not utilize fully all the components of the Bayesian approach.

Forecasting—the Bayesian approach

1983 ◽  
Vol 110 (01) ◽  
pp. 183-203
Author(s):  
R. J. Verrall
Keyword(s):  
Time Series ◽  
Bayesian Approach ◽  
Bayesian Forecasting ◽  
Statistical Forecasting ◽  
The Future ◽  
Data Points ◽  
The Bayesian Approach

For most people, statistical forecasting means modelling time series using the methods described by Box and Jenkins (1970). A Box and Jenkins model requires a large number of known data points before it can be properly chosen and, since it cannot be changed without again observing another batch of input, is only of use if the data conforms in the future to the chosen model. This means that this style of forecasting is essentially rigid, unadaptable and of limited use in practice. This paper sets out, with the aid of the examples, the essentials and some applications of Bayesian forecasting as developed by Harrison and Stevens (1976).

Bayesian Statistics

2021 ◽  
pp. 165-180
Author(s):  
Timothy E. Essington
Keyword(s):  
Parameter Estimation ◽  
Statistical Analysis ◽  
Model Selection ◽  
Bayesian Statistics ◽  
Bayes Theorem ◽  
Bayesian Parameter Estimation ◽  
Bayesian Approaches ◽  
Frequentist Statistics ◽  
The Difference ◽  
The Bayesian Approach

The chapter “Bayesian Statistics” gives a brief overview of the Bayesian approach to statistical analysis. It starts off by examining the difference between frequentist statistics and Bayesian statistics. Next, it introduces Bayes’ theorem and explains how the theorem is used in statistics and model selection, with the prosecutor’s fallacy given as a practice example. The chapter then goes on to discuss priors and Bayesian parameter estimation. It concludes with some final thoughts on Bayesian approaches. The chapter does not answer the question “Should ecologists become Bayesian?” However, to the extent that alternative models can be posed as alternative values of parameters, Bayesian parameter estimation can help assign probabilities to those hypotheses.

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.

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.

SYNTHESIS OF EVIDENCE FOR REIMBURSEMENT DECISIONS: A BAYESIAN REANALYSIS

2014 ◽  
Vol 30 (4) ◽  
pp. 438-445
Author(s):  
Willem Woertman ◽  
Rene Sluiter ◽  
Gert Jan van der Wilt
Keyword(s):  
Experimental Data ◽  
Bayesian Approach ◽  
Bayesian Methods ◽  
Policy Making ◽  
Evidence Based ◽  
Informational Needs ◽  
Policy Makers ◽  
Science And Policy ◽  
Standard Methods ◽  
The Bayesian Approach

Objectives: The aim of this study was to compare Bayesian methods with the standard methods that are used for evidence-based policy making.Methods: We performed a Bayesian reanalysis of the data underlying a reimbursement advice by the Dutch National Health Insurance Board (CVZ) regarding the anti-diabetic drug exenatide (an alternative to insulin). We synthesized evidence from various sources that was available when the CVZ advice was drafted: expert opinion (as elicited from internists), experimental data (from direct comparison studies), and observational data. Subsequently, the original frequentist results and the results from the Bayesian reanalysis were compared in terms of outcomes and interpretations. These results were presented in a meeting with staff from CVZ, whose opinions about the usefulness of a Bayesian approach were assessed using a questionnaire.Results: The Bayesian approach yields outcomes that summarize different pieces of evidence, which would have been difficult to obtain otherwise. Moreover, there are conceptual differences, and the Bayesian approach allows for determining probabilities of clinically relevant differences. The staff at CVZ were fairly positive with respect to the use of Bayesian methods, although practical barriers were also seen as important.Conclusions: The Bayesian outcomes are different and could be more suited to the informational needs of policy makers. The response from staff at CVZ provides some support for this statement, but more research at the interface of science and policy is needed.

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