scholarly journals A Bayesian Approach to Item Development: Working with Alternative Item Types

2020 ◽  
Author(s):  
Frank Dyer

Describes a Bayesian procedure for weighting and aggregating individual predictors based on drawings and associations. Discusses forensic psychological applications of the procedure, especially in situations where impression management is a factor. Highlights conceptual differences between Bayesian methods and IRT and CTT systems.

2020 ◽  
Author(s):  
Frank Dyer

Describes a Bayesian procedure for weighting and aggregating individual predictors based on drawings and associations. Discusses forensic psychological applications of the procedure, especially in situations where impression management is a factor. Highlights conceptual differences between Bayesian methods and IRT and CTT systems.


2016 ◽  
Vol 59 (2) ◽  
pp. 243-248 ◽  
Author(s):  
Hafedh Ben Zaabza ◽  
Abderrahmen Ben Gara ◽  
Hedi Hammami ◽  
Mohamed Amine Ferchichi ◽  
Boulbaba Rekik

Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent environmental variances estimated by REML were smaller than those obtained by the Bayesian analysis. Inversely, REML estimates of the residual variances were larger than Bayesian estimates. Genetic and permanent correlation estimates were on the other hand comparable by both REML and Bayesian methods with permanent environmental being larger than genetic correlations. Results from this study confirm previous reports on genetic parameters for milk traits in Tunisian Holsteins and suggest that a multi-trait approach can be an alternative for implementing a routine genetic evaluation of the Tunisian dairy cattle population.


2019 ◽  
Vol 35 (02) ◽  
pp. 321-338
Author(s):  
Bengt Autzen

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.


2018 ◽  
Vol 10 (10) ◽  
pp. 3671
Author(s):  
Jongseon Jeon ◽  
Suneung Ahn

The work proposed a reliability demonstration test (RDT) process, which can be employed to determine whether a finite population is accepted or rejected. Bayesian and non-Bayesian approaches were compared in the proposed RDT process, as were lot and sequential sampling. One-shot devices, such as bullets, fire extinguishers, and grenades, were used as test targets, with their functioning state expressible as a binary model. A hypergeometric distribution was adopted as the likelihood function for a finite population consisting of binary items. It was demonstrated that a beta-binomial distribution was the conjugate prior of the hypergeometric likelihood function. According to the Bayesian approach, the posterior beta-binomial distribution is used to decide on the acceptance or rejection of the population in the RDT. The proposed method in this work could be used to select item providers in a supply chain, who guarantee a predetermined reliability target and confidence level. Numerical examples show that a Bayesian approach with sequential sampling has the advantage of only requiring a small sample size to determine the acceptance of a finite population.


Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1302
Author(s):  
Longfei Xie ◽  
Fengri Li ◽  
Lianjun Zhang ◽  
Faris Rafi Almay Widagdo ◽  
Lihu Dong

Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SUR) was used to fit the simultaneous equations of four tree components (i.e., stem, branch, foliage, and root). The model parameters were estimated by feasible generalized least squares (FGLS) and Bayesian methods using either non-informative priors or informative priors. The results showed that adding tree height to the model systems improved the model fitting and performance for the stem, branch, and foliage biomass models, but much less for the root biomass models. The Bayesian methods on the SUR models produced narrower 95% prediction intervals than did the classical FGLS method, indicating higher computing efficiency and more stable model predictions, especially for small sample sizes. Furthermore, the Bayesian methods with informative priors performed better (smaller values of deviance information criterion (DIC)) than those with the non-informative priors. Therefore, our results demonstrated the advantages of applying the Bayesian methods on the SUR biomass models, not only obtaining better model fitting and predictions, but also offering the assessment and evaluation of the uncertainties for constructing and updating tree biomass models.


2019 ◽  
Vol 15 (2) ◽  
pp. 20180632 ◽  
Author(s):  
Martin R. Smith

Phylogenetic analysis aims to establish the true relationships between taxa. Different analytical methods, however, can reach different conclusions. In order to establish which approach best reconstructs true relationships, previous studies have simulated datasets from known tree topologies, and identified the method that reconstructs the generative tree most accurately. On this basis, researchers have argued that morphological datasets should be analysed by Bayesian approaches, which employ an explicit probabilistic model of evolution, rather than parsimony methods—with implied weights parsimony sometimes identified as particularly inaccurate. Accuracy alone, however, is an inadequate measure of a tree's utility: a fully unresolved tree is perfectly accurate, yet contains no phylogenetic information. The highly resolved trees recovered by implied weights parsimony in fact contain as much useful information as the more accurate, but less resolved, trees recovered by Bayesian methods. By collapsing poorly supported groups, this superior resolution can be traded for accuracy, resulting in trees as accurate as those obtained by a Bayesian approach. By contrast, equally weighted parsimony analysis produces trees that are less resolved and less accurate, leading to less reliable evolutionary conclusions.


Author(s):  
Kate E. Walton ◽  
Justine Radunzel ◽  
Raeal Moore ◽  
Jeremy Burrus ◽  
Cristina Anguiano-Carrasco ◽  
...  

Author(s):  
Matthias Breuer ◽  
Harm H. Schütt

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.


2020 ◽  
Vol 27 (2) ◽  
pp. 22-31
Author(s):  
R.K. Ogundeji ◽  
J.N. Onyeka-Ubaka

Election process and results in many countries have resulted in both political and economic instability of that country. Fair and credible election process and results must be evidence-based and statistical proven. This study employed a Bayesian procedure for the validation of election results. Based on Nigerian 2011 and 2015 presidential election results, Bayesian credible intervals were obtained to assess the credibility of Nigeria presidential election results. The study explores Bayesian methods using a Bayesian model called beta-binomial conjugate model to compute posterior probability of electoral votes cast and confirm if these votes are within Bayesian credible intervals. The results obtained showed that election outcomes for the two major political parties in Nigeria 2011 presidential election are not within Bayesian credible bounds while 2015 presidential election results are within computed Bayesian credible bounds. Also, in contrast to frequentist approach, applied Bayesian methodology exhibited smaller variance which is an indication that Bayesian approach is more efficient. Thus, for election to be fair, credible and acceptable by the electorates, Bayesian approach can be used to validate electoral process and results. Keywords: Bayesian Methods, Bayesian Credible Intervals, Beta-Binomial Model, Empirical Bayes, Nigeria Presidential Elections.


2019 ◽  
Vol 45 (1) ◽  
pp. 47-68 ◽  
Author(s):  
Scott M. Lynch ◽  
Bryce Bartlett

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.


Sign in / Sign up

Export Citation Format

Share Document