scholarly journals Bayesian Benefits for Binomial Applications in Practice

2019 ◽  
pp. 1-9
Author(s):  
Frank Tuyl ◽  
Peter Howley
Keyword(s):  
Bayesian Methods ◽  
Prior Information ◽  
Statistical Software ◽  
Practical Applications ◽  
Bayesian Techniques ◽  
Bayesian Paradigm ◽  
The World ◽  
Binomial Parameter ◽  
Authentic Learning Experiences ◽  
The Bayesian Approach

Introduction: When it comes to the practice, and teaching, of statistics, the world has primarily focused on what is known as classical or frequentist methods, rather than Bayesian methods. Scope of the Study: This paper demonstrates some beneficial properties of Bayesian methods within the commonly practiced domain of inference by utilizing consultancy case studies, one concerning an unusual sample size question and one on the detection of mail items with high biosecurity risk material. Methods: We introduce through practical applications two more aspects of the Bayesian approach which we believe are invaluable to practitioners and instructors. Having in mind readers who may be less familiar with statistical software, we have added Excel instructions which are easy to translate for those who are familiar with any such software. Findings: These cases reflect two valuable aspects for both practitioners and instructors which are unique to the Bayesian paradigm. They are: 1. including prior information to improve inference and how to apply sensitivity analysis to this inclusion and 2. the effortless inference for functions of parameters, compared with frequentist approaches. These examples involving the binomial parameter have not been considered from this perspective before, are of significant practical value and thus benefit students and instructors of courses teaching Bayesian techniques and endeavoring to include authentic learning experiences.

A Predictive Bayesian Approach to Multistate Reliability Analysis

2003 ◽  
Vol 10 (03) ◽  
pp. 221-234 ◽  
Author(s):  
T. Aven ◽  
A. Hjorteland
Keyword(s):  
Reliability Analysis ◽  
Bayesian Approach ◽  
Probability Models ◽  
Bayesian Paradigm ◽  
Bayesian Procedures ◽  
Starting Point ◽  
The World ◽  
The Bayesian Approach

In this paper we discuss how to implement a Bayesian thinking for multistate reliability analysis. The Bayesian paradigm comprises a unified and consistent framework for analysing and expressing reliability, but in our view the standard Bayesian procedures gives too much emphasis on probability models and inference on fictional parameters. We believe that there is a need for a rethinking on how to implement the Bayesian approach, and in this paper we present and discuss such a rethinking for multistate reliability analysis. The starting point of the analysis should be observable quantities, expressing states of the world, not fictional parameters.

Symmetry and Bayesian Function Estimation1

2005 ◽  
Vol 56 (1-4) ◽  
pp. 57-80
Author(s):  
Jean-Francois Angers ◽  
Peter T. Kim
Keyword(s):  
Bayesian Methods ◽  
Riemannian Manifolds ◽  
Prior Information ◽  
Beltrami Operator ◽  
Spectral Geometry ◽  
Function Estimation ◽  
Bayes Estimators ◽  
Nonparametric Function ◽  
Nonparametric Function Estimation ◽  
The Bayesian Approach

Summary This paper develops Bayesian function estimation on compact Riemannian manifolds. The approach is to combine Bayesian methods along with aspects of spectral geometry associated with the Laplace-Beltrami operator on Riemannian manifolds. Although frequentist nonparametric function estimation in Euclidean space abound, to date, no attempt has been made with respect to Bayesian function estimation on a general Riemannian manifold. The Bayesian approach to function estimation is very natural for manifolds because one can elicit very specific prior information on the possible symmetries in question . One can then establish Bayes estimators that possess built in symmetries. A detailed analysis for the 2–sphere is provided.

scholarly journals Principles of Statistical Inference: Likelihood and the Bayesian Paradigm

2010 ◽  
Vol 16 ◽  
pp. 1-18 ◽  
Author(s):  
Steve C. Wang
Keyword(s):  
Maximum Likelihood ◽  
Statistical Inference ◽  
Bayesian Approach ◽  
Subjective Probability ◽  
Prior Information ◽  
Maximum Likelihood Estimators ◽  
Likelihood Ratio Tests ◽  
Bayesian Paradigm ◽  
Point Estimators ◽  
The Bayesian Approach

We review two foundations of statistical inference, the theory of likelihood and the Bayesian paradigm. We begin by applying principles of likelihood to generate point estimators (maximum likelihood estimators) and hypothesis tests (likelihood ratio tests). We then describe the Bayesian approach, focusing on two controversial aspects: the use of prior information and subjective probability. We illustrate these analyses using simple examples.

Bayesian Analysis

2009 ◽  
Author(s):  
Andrew D. Martin
Keyword(s):  
Social Sciences ◽  
Monte Carlo ◽  
Political Science ◽  
Bayesian Methods ◽  
Change Point ◽  
Prior Information ◽  
Mcmc Methods ◽  
The Social ◽  
Change Point Models ◽  
The Bayesian Approach

This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian inference, and a discussion of the future of Bayesian methods in political science. One area ripe for research is the use of prior information in statistical analyses. Mixture models and those with discrete parameters (such as change point models in the time-series context) are completely underutilized in political science.

INCORPORATION OF GENUINE PRIOR INFORMATION IN COST-EFFECTIVENESS ANALYSIS OF CLINICAL TRIAL DATA

2002 ◽  
Vol 18 (4) ◽  
pp. 782-790 ◽  
Author(s):  
John W. Stevens ◽  
Anthony O'Hagan
Keyword(s):  
Clinical Trial ◽  
Bayesian Methods ◽  
Prior Information ◽  
Clinical Trial Data ◽  
Final Analysis ◽  
Trial Data ◽  
Decision Makers ◽  
Scientific Rigor ◽  
Effectiveness Analysis ◽  
The Bayesian Approach

The Bayesian approach to statistics has been growing rapidly in popularity as an alternative to the frequentist approach in the appraisal of heathcare technologies in clinical trials. Bayesian methods have significant advantages over classical frequentist statistical methods and the presentation of evidence to decision makers. A fundamental feature of a Bayesian analysis is the use of prior information as well as the clinical trial data in the final analysis. However, the incorporation of prior information remains a controversial subject that provides a potential barrier to the acceptance of practical uses of Bayesian methods. The pur pose of this paper is to stimulate a debate on the use of prior information in evidence submitted to decision makers. We discuss the advantages of incorporating genuine prior information in cost-effectivene ss analyses of clinical trial data and explore mechanisms to safeguard scientific rigor in the use of such prior information.

Towards a new convergence between Anglo-American and Russian literary linguistics: “mind style” and “kartina mira”

2010 ◽  
Vol 39 (2) ◽  
Author(s):  
Ben W. Dhooge
Keyword(s):  
Mutual Influence ◽  
Stylistic Analysis ◽  
Literary Language ◽  
Practical Applications ◽  
The World ◽  
Russian Literary ◽  
Anglo American ◽  
1960S And 1970S ◽  
The 1960S ◽  
Mutually Independent

AbstractAnglo-American and Russian stylistics influenced each other substantially in the 1960s and 1970s. From the 1980s on, however, this fruitful mutual influence came to an end. The two schools started to grow apart, but despite that, they would develop almost parallel to each other, displaying many theoretical and methodological similarities. The present paper illustrates this by highlighting one such specificity – the idea of the possible reflection of one's conceptualization of the world in the use of literary language, and the possibility of reconstructing that conceptualization by means of a stylistic analysis (‘mind style’–‘kartina mira’). By comparing the Anglo-American and Russian theories on the topic, it is shown that the separately evolved conceptions are similar and even complement each other: the differences between them clarify and help solve possible theoretical and methodological gaps. Moreover, the juxtaposition of both conceptions allows us to perfect the notion of ‘mind style’ and its practical applications. A similar approach to other conceptions and tendencies in current seemingly mutually independent Anglo-American and Russian stylistics have the same potential, and may lead to a new convergence between the two schools.

scholarly journals Bayesian sample size re-estimation using power priors

2018 ◽  
Vol 28 (6) ◽  
pp. 1664-1675 ◽  
Author(s):  
TB Brakenhoff ◽  
KCB Roes ◽  
S Nikolakopoulos
Keyword(s):  
Sample Size ◽  
Prior Information ◽  
Controlled Trial ◽  
Small Populations ◽  
Small Pilot Study ◽  
Randomized Controlled ◽  
Bayesian Paradigm ◽  
Operational Characteristics ◽  
True Variance ◽  
Bayesian Sample Size

The sample size of a randomized controlled trial is typically chosen in order for frequentist operational characteristics to be retained. For normally distributed outcomes, an assumption for the variance needs to be made which is usually based on limited prior information. Especially in the case of small populations, the prior information might consist of only one small pilot study. A Bayesian approach formalizes the aggregation of prior information on the variance with newly collected data. The uncertainty surrounding prior estimates can be appropriately modelled by means of prior distributions. Furthermore, within the Bayesian paradigm, quantities such as the probability of a conclusive trial are directly calculated. However, if the postulated prior is not in accordance with the true variance, such calculations are not trustworthy. In this work we adapt previously suggested methodology to facilitate sample size re-estimation. In addition, we suggest the employment of power priors in order for operational characteristics to be controlled.

scholarly journals Bringing the World to the Classroom: Teaching Statistics and Programming in a Project-Based Setting

2021 ◽  
pp. 1-5
Author(s):  
Cosima Meyer
Keyword(s):  
Data Management ◽  
Data Science ◽  
Classroom Teaching ◽  
Statistical Software ◽  
Inverted Classroom ◽  
Typical Data ◽  
The World ◽  
Virtual Classes ◽  
Teaching Statistics ◽  
Introductory Class

ABSTRACT This article introduces how to teach an interactive, one-semester-long statistics and programming class. The setting also can be applied to shorter and longer classes as well as introductory and advanced courses. I propose a project-based seminar that also encompasses elements of an inverted classroom. As a result of this combination, the seminar supports students’ learning progress and also creates engaging virtual classes. To demonstrate how to apply a project-based seminar setting to teaching statistics and programming classes, I use an introductory class to data wrangling and management with the statistical software program R. Students are guided through a typical data science workflow that requires data management and data wrangling and concludes with visualizing and presenting first research results during a simulated mini-conference.

scholarly journals Web Semântica no repositório BRAPCI

2016 ◽  
Vol 9 (2) ◽  
Author(s):  
Nátalia NAKANO ◽  
Talita Cristina da SILVA ◽  
Maria José Vicentini JORENTE ◽  
José Eduardo SANTARÉM SEGUNDO
Keyword(s):  
Semantic Web ◽  
Information Science ◽  
Research Problem ◽  
Human Knowledge ◽  
Practical Applications ◽  
The World ◽  
Working Together ◽  
The Subject ◽  
Web Research ◽  
Productive Authors

In 2001 Tim Berners-Lee revealed to the world what he wanted for the future of Web - man and machine working together to develop complex tasks, and that the Web could leverage the way human knowledge is acquired. Since then researchers from different fields of knowledge have engaged in scientific and empirical research to make this dream come true. In this context, the research problem of this article is established: What is the current situation of Semantic Web research in Brazil in Information Science? Who are the researchers of this theme in our country? What are the institutions that support these studies? The present study aimed at listing the most productive authors, institutions that support their research and the specific issues of their investigations. We conducted a literature review in Base de Dados Referencial de Artigos de Periódicos em Ciência da Informação (BRAPCI). We retrieved 41 articles, excluded five for not belonging to Brazilian authors and Brazilian institutions. From the analysis of this corpus, we realized the need to include additional keywords to better understanding of specific studies encompassed by the theme. Thus, we included the keywords: SPARQL, SKOS, RDF and ontology. It was concluded that the studies on the Semantic Web under the aegis of Information Science mostly perform theoretical and philosophical studies, while the computer science professionals seek practical applications of the topic. It was also concluded that a study including other databases could reveal other authors and institutions relevant to the subject of study.

scholarly journals Bayesian approach to evaluation of distance courses

2014 ◽  
Vol 55 ◽  
Author(s):  
Jonas Mockus ◽  
Irina Vinogradova
Keyword(s):  
Bayesian Approach ◽  
Prior Information ◽  
Uncertain Data ◽  
Distribution Functions ◽  
Fuzzy Sets Theory ◽  
Distance Courses ◽  
Sets Theory ◽  
Education Courses ◽  
The Bayesian Approach

Many real applications are using uncertain data This include expert decisions based on their subjective opinions, The uncertainty can be evaluated applying fuzzy sets theory or the methods of mathematical statistics. In this paper it is proposed to use the Bayesian approach by different distribution functions defining the expert opinion and some prior information. The results are illustrated evaluating the quality of distant education courses.

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