Manegerial Decesion approachs - Essay Example

MANAGERIAL DECISION APPROACHES By Location Managerial Decision Approaches Marketers are constantly faced withuncertainties that they must address if they are to be successful. The Bayes theorem approach has been used to address such uncertainties. The Bayes theorem is an approach that relies on probability statistics. Many people recognize its potential to present results at any given time as long as there are real values attached to each event. The Bayesian approach can combine prior information with the currently available information when analysing an event of interest.

The Bayesian approach relies on making inferences using the probability formulae. The Bayesian approach has been regarded highly because it presents the possibility of using prior information and data to establish the decision-making framework (Medow & Lucey 2011, p. 166). Statisticians only need to analyse parameters effectively and rely on the prior distribution to determine the future analysis. The complete Bayesian analysis brings into concept the prior probabilities, new information, as well as posterior probabilities (Jung In & Yongku 2015, p. 1444). The Bayesian approach is commonly used when computing the future possibilities of posterior probabilities the outcomes obtained from the Bayesian calculations can be used in decision-making.

This paper will evaluate the Bayesian approach and highlight its application in creating a travel company. Additionally, the paper will highlight the potential bias that is evident when using the Bayesian approach. One of the advantages of the Bayesian approach is that it relies on prior information, a factor that makes sense to many decision makers. It is easy for decision makers to rely on the available information to determine what may happen in the future. In addition, when adequate prior information is available, the decision maker may not face the compulsion of undertaking detailed calculations.

In many cases, the availability of reliable prior information may save a decision maker from numerous tests. The Bayesian approach presents decision makers with realistic confidence intervals that make the outcomes more valid (Medow & Lucey 2011, p. 164). Although the Bayesian analysis is a preferred approach when analysing events and parameters, it has certain disadvantages. The approach does not specify how a statistician should identify the prior distribution. In many cases, lack of experience can lead to misleading outcomes.

In other instances, the Bayesian analysis yields posterior distributions that are highly dependent on the prior distributions. In such cases, there may be conflicts regarding the choice of the prior distributions. The Bayesian approach requires high computational costs that many organizations may not be able to address. Although the framework is easy, proper calculations may take days before the outcomes are determined (Jung In & Yongku 2015, p. 1449). Since there is no distinct approach of determining the prior information, a decision maker may have varied outcomes depending on the approaches used.

The Bayesian theorem approach can be used in decision making when building a house for profit. Through the identification of prior distribution, current information, and the posterior distribution, it becomes possible to determine whether building a house will be profitable. The Bayesian analysis can be used to analyse different parameters of the housing industry. Many industries rely on the likelihood principle to determine the expected reception of a company in the market. For this reason, the Bayesian approach can be of significant importance when building a house for profit (Medow & Lucey 2011, p. 166). Bibliography Jung In, S, & Yongku, K 2015, Approximated Information Analysis in Bayesian Inference, Entropy, 17, 3, pp.

1441-1451, Academic Search Complete, EBSCOhost, viewed 20 May 2015. Medow, M, & Lucey, C 2011, A qualitative approach to Bayes theorem, Evidence-Based Medicine, 16, 6, pp. 163-167, MEDLINE with Full Text, EBSCOhost, viewed 20 May 2015.