Bayesian Analysis


Bayesian statistics is a subset of the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief - Bayesian probabilities. The concept reviews the origins and application of this statistical approach.

Technique Overview

Bayesian Analysis

Bayesian Analysis Definition

The goal of Bayesian analysis is “to translate subjective forecasts into mathematical probability curves in situations where there are no normal statistical probabilities because alternatives are unknown or have not been tried before” (Armstrong, 2003:633). Bayesian analysis aims to update probabilities in the light of new evidence via Bayes' theorem (Jackman, 2009).

Bayesian Analysis Description *

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Business Evidence

Strengths, weaknesses and examples of Bayesian Analysis *

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Business Application

Implementation, success factors and measures of Bayesian Analysis *

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Professional Tools

Bayesian Analysis videos and downloads *

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Further Reading

Bayesian Analysis web and print resources *

Bayesian Analysis references (4 of up to 20) *

  • Andrews, M. and Whittaker, J.A. (2006) How to Break Web Software. Pearson Education, Boston, MA.
  • Armstrong, M. (2003) A Handbook of Management Techniques. Kogan Page, London.
  • Beri, G.C. (2008) Marketing Research. Tata McGraw-Hill, New Delhi.
  • Blattberg, R.C., Kim, B.-D. and Neslin, S.A. (2008) Database Marketing: Analyzing and Managing Customers. Springer, New York.

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Related Concept: Collating and Formatting Data

 Before any analysis can happen, data needs to be collated from correct sources and formatted so it follows clear organisational standards. Research shows that inconsistent structures and formats make data harder to combine, process and trust (Jagadish et al., 2014). Collating and formatting ensure the dataset is clean, consistent and ready for use.