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Article
Publication date: 7 June 2021

Carol K.H. Hon, Chenjunyan Sun, Bo Xia, Nerina L. Jimmieson, Kïrsten A. Way and Paul Pao-Yen Wu

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to…

Abstract

Purpose

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

Design/methodology/approach

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Findings

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

Originality/value

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

Details

Engineering, Construction and Architectural Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0969-9988

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Book part
Publication date: 12 November 2014

Matthew Lindsey and Robert Pavur

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the…

Abstract

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

Details

Advances in Business and Management Forecasting
Type: Book
ISBN: 978-1-78441-209-8

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Book part
Publication date: 19 November 2014

Enrique Martínez-García and Mark A. Wynne

We investigate the Bayesian approach to model comparison within a two-country framework with nominal rigidities using the workhorse New Keynesian open-economy model of…

Abstract

We investigate the Bayesian approach to model comparison within a two-country framework with nominal rigidities using the workhorse New Keynesian open-economy model of Martínez-García and Wynne (2010). We discuss the trade-offs that monetary policy – characterized by a Taylor-type rule – faces in an interconnected world, with perfectly flexible exchange rates. We then use posterior model probabilities to evaluate the weight of evidence in support of such a model when estimated against more parsimonious specifications that either abstract from monetary frictions or assume autarky by means of controlled experiments that employ simulated data. We argue that Bayesian model comparison with posterior odds is sensitive to sample size and the choice of observable variables for estimation. We show that posterior model probabilities strongly penalize overfitting, which can lead us to favor a less parameterized model against the true data-generating process when the two become arbitrarily close to each other. We also illustrate that the spillovers from monetary policy across countries have an added confounding effect.

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Book part
Publication date: 31 January 2015

Davy Janssens and Geert Wets

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application…

Abstract

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application, we will use decision rules to support the decision-making of the model instead of principles of utility maximization, which means our work can be interpreted as an application of the concept of bounded rationality in the transportation domain. In this chapter we explored a novel idea of combining decision trees and Bayesian networks to improve decision-making in order to maintain the potential advantages of both techniques. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of a travel demand model with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

Details

Bounded Rational Choice Behaviour: Applications in Transport
Type: Book
ISBN: 978-1-78441-071-1

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Book part
Publication date: 1 January 2008

Arnold Zellner

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making…

Abstract

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

Details

Bayesian Econometrics
Type: Book
ISBN: 978-1-84855-308-8

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Article
Publication date: 29 November 2019

A. George Assaf and Mike G. Tsionas

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

Abstract

Purpose

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

Design/methodology/approach

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

Findings

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

Research limitations/implications

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

Originality/value

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.

Details

International Journal of Contemporary Hospitality Management, vol. 32 no. 4
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 13 March 2017

Lei Xue, Changyin Sun and Fang Yu

The paper aims to build the connections between game theory and the resource allocation problem with general uncertainty. It proposes modeling the distributed resource…

Abstract

Purpose

The paper aims to build the connections between game theory and the resource allocation problem with general uncertainty. It proposes modeling the distributed resource allocation problem by Bayesian game. During this paper, three basic kinds of uncertainties are discussed. Therefore, the purpose of this paper is to build the connections between game theory and the resource allocation problem with general uncertainty.

Design/methodology/approach

In this paper, the Bayesian game is proposed for modeling the resource allocation problem with uncertainty. The basic game theoretical model contains three parts: agents, utility function, and decision-making process. Therefore, the probabilistic weighted Shapley value (WSV) is applied to design the utility function of the agents. For achieving the Bayesian Nash equilibrium point, the rational learning method is introduced for optimizing the decision-making process of the agents.

Findings

The paper provides empirical insights about how the game theoretical model deals with the resource allocation problem uncertainty. A probabilistic WSV function was proposed to design the utility function of agents. Moreover, the rational learning was used to optimize the decision-making process of agents for achieving Bayesian Nash equilibrium point. By comparing with the models with full information, the simulation results illustrated the effectiveness of the Bayesian game theoretical methods for the resource allocation problem under uncertainty.

Originality/value

This paper designs a Bayesian theoretical model for the resource allocation problem under uncertainty. The relationships between the Bayesian game and the resource allocation problem are discussed.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 1
Type: Research Article
ISSN: 1756-378X

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Article
Publication date: 4 September 2009

Giovanni Celano, Antonio Costa, Sergio Fichera and Enrico Trovato

The search of the optimal economic design of the Bayesian adaptive control charts for finite production runs can be a long and tedious procedure due to the intrinsic…

Abstract

Purpose

The search of the optimal economic design of the Bayesian adaptive control charts for finite production runs can be a long and tedious procedure due to the intrinsic structure of the optimization problem, which requires a dynamic programming approach to select the best decision at each sampling epoch during the production horizon of the process. This paper aims to propose a new efficient procedure implementing a genetic algorithm neighbourhood search scheme embedded within the dynamic programming procedure with the aim of reducing the computational burden and achieving significant cost savings in the chart implementation.

Design/methodology/approach

The efficiency of the developed procedure has been verified through a comparison with another existing exhaustive approach working exclusively on one‐sided Bayesian control charts; then, it has been extended to the design of two‐sided Bayesian control charts.

Findings

The proposed procedure implementing the genetic algorithm neighbourhood search is very fast and efficient in detecting optimal solutions: it allows significant quality control cost savings to be achieved during the Bayesian charts implementation thanks to the possibility of investigating larger spaces of decisions than the existing optimization procedures.

Practical implications

With reference to discrete part manufacturing, where the assumption of finite production runs is often realistic, the design and implementation of adaptive Bayesian control charts by means of the proposed procedure allows significant cost savings to be achieved with respect to the fixed parameters Shewhart charts.

Originality/value

The exhaustive optimization procedure cannot be executed in a reasonable computational time when the space of decisions to select Bayesian chart design parameters significantly enlarges, which is the case of two‐sided control charts. The paper documents the proposed procedure which overcomes this problem and allows the two‐sided Bayesian chart to be designed and proposed as an efficient means to monitor short production runs.

Details

International Journal of Quality & Reliability Management, vol. 26 no. 8
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 5 July 2018

Harindranath R.M. and Jayanth Jacob

This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis…

Abstract

Purpose

This paper aims to popularize the Bayesian methods among novice management researchers. The paper interprets the results of Bayesian method of confirmatory factor analysis (CFA), structural equation modelling (SEM), mediation and moderation analysis, with the intention that the novice researchers will apply this method in their research. The paper made an attempt in discussing various complex mathematical concepts such as Markov Chain Monte Carlo, Bayes factor, Bayesian information criterion and deviance information criterion (DIC), etc. in a lucid manner.

Design/methodology/approach

Data collected from 172 pharmaceutical sales representatives were used. The study will help the management researchers to perform Bayesian CFA, Bayesian SEM, Bayesian moderation analysis and Bayesian mediation analysis using SPSS AMOS software.

Findings

The interpretation of the results of Bayesian CFA, Bayesian SEM and Bayesian mediation analysis were discussed.

Practical implications

The management scholars are non-statisticians and are not much aware of the benefits offered by Bayesian methods. Hitherto, the management scholars use predominantly traditional SEM in validating their models empirically, and this study will give an exposure to “Bayesian statistics” that has practical advantages.

Originality/value

This is one paper, which discusses the following four concepts: Bayesian method of CFA, SEM, mediation and moderation analysis.

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Article
Publication date: 21 February 2018

Franz T. Lohrke, Charles M. Carson and Archie Lockamy

The purpose of this paper is to review Bayesian analysis in recent entrepreneurship research to assess how scholars have employed these methods to study the…

Abstract

Purpose

The purpose of this paper is to review Bayesian analysis in recent entrepreneurship research to assess how scholars have employed these methods to study the entrepreneurship process. Researchers in other business fields (e.g. management science, marketing, and finance) have increasingly employed Bayesian methods to study issues like decision making. To date, however, Bayesian methods have seen only limited use in entrepreneurship research.

Design/methodology/approach

After providing a general overview of Bayesian methods, this study examines how extant entrepreneurship research published in leading journals has employed Bayesian analysis and highlights topics these studies have investigated most frequently. It next reviews topics that scholars from other business disciplines have investigated using these methods, focusing on issues related to decision making, in particular.

Findings

Only seven articles published in leading management and entrepreneurship journals between 2000 and 2016 employed or discussed Bayesian methods in depth when studying the entrepreneurship process. In addition, some of these studies were conceptual.

Research limitations/implications

This review suggests that Bayesian methods may provide another important tool for researchers to employ when studying decision making in high uncertainty situations or the impact of entrepreneurial experience on decision making over time.

Originality/value

This review demonstrates that Bayesian analysis may be particularly appropriate for entrepreneurship research. By employing these methods, scholars may gain additional insights into entrepreneurial phenomenon by allowing researchers to examine entrepreneurial decision making. Through this review and these recommendations, this study hopes to encourage greater Bayesian analysis usage in future entrepreneurship research.

Details

Management Decision, vol. 56 no. 5
Type: Research Article
ISSN: 0025-1747

Keywords

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