Uncertainty in making decisions
Generalizing something that is specific from a statistical standpoint, is the problem of induction, and that can cause uncertainty in making decisions (Spiegelhalter & Rice, 2009). Uncertainty in making a decision could also arise from not knowing how to incorporate new data with old assumptions (Hubbard, 2010).
According to Hubbard (2010) conventional statistics assumes:
(1) The researcher has no prior information about the range of possible values (which is never true) or,
(2) The researcher does have prior knowledge that the distribution of the population and it is never any of the messy ones (which is not true more often than not)
Thus, knowledge before data collection and the knowledge gained from data collection doesn’t tell the full story until they are combined, hence the need for Bayes’ analysis (Hubbard, 2010). Bayes’ theory can be reduced to a conditional probability that aims to take into account prior knowledge, but updates itself when new data becomes available (Hubbard, 2010; Smith, 2015; Spiegelhalter & Rice, 2009; Yudkowsky, 2003). Bayesian analysis avoids overconfidence and underconfidence from ignoring prior data or ignoring new data (Hubbard, 2010), through the implementation of the equation below:
Where P(hypothesis|data) is the posterior data, P(hypothesis) is the true probability of the hypothesis/distribution before the data is introduced, P(data) marginal probability, and P(data|hypothesis) is the likelihood that the hypothesis/distribution is still true after the data is introduced (Hubbard, 2010; Smith, 2015; Spiegelhalter & Rice, 2009; Yudkowsky, 2003). This forces the researcher to think about the likelihood that different and new observations could impact a current hypothesis (Hubbard, 2010). Equation (1) shows that evidence is usually a result of two conditional probabilities, where the strongest evidence comes from a low probability that the new data could have led to X (Yudkowsky, 2003). From these two conditional probabilities, the resultant value is approximately the average from that of the prior assumptions and the new data gained (Hubbard, 2010; Smith, 2015). Smith (2015) describe this approximation in the following simplified relationship (equation 2):
Therefore, from equation (2) the type of prior assumptions influence the posterior resultant. Prior distributions come from Uniform, Constant, or Normal distribution that results in a Normal posterior distribution and a Beta or Binomial distribution results in a Beta posterior distribution (Smith, 2015). To use Bayesian Analysis one must take into account the analysis’ assumptions.
Basic Assumptions of Bayesian Analysis
Though these three assumptions are great to have for Bayesian Analysis, it has been argued that they are quite unrealistic when real life data, particularly unstructured text-based data (Lewis, 1998; Turhan & Bener, 2009):
- Each of the new data samples is independent of each other and identically distributed (Lewis, 1998; Nigam & Ghani, 2000; Turhan & Bener, 2009)
- Each attribute has equation importance (Turhan & Bener, 2009)
- The new data is compatible with the target posterior (Nigam & Ghani, 2000; Smith 2015).
Applications of Bayesian Analysis
There are typically three main situations where Bayesian Analysis is used (Spiegelhalter, & Rice, 2009):
- Small data situations: The researcher has no choice but to include prior quantitative information, because of a lack of data, or lack of a distribution model.
- Moderate size data situations: The researcher has multiple sources of data. They can create a hierarchical model on the assumption of similar prior distributions
- Big data situations: where there are huge join probability models, with 1000s of data points or parameters, which can then be used to help make inferences of unknown aspects of the data
Pros and Cons
Applying Bayesian Analytics to data has its advantages and disadvantages. Those Advantages and Disadvantages with Bayesian Analysis as identified by SAS (n.d.) are:
+ Allows for a combination of prior information with data, for a strong decision-making
+ No reliance on asymptotic approximation, thus the inferences are conditional on the data
+ Provides easily interpretive results.
– Posteriors are heavily influenced by their priors.
– This method doesn’t help the researcher to select the proper prior, given how much influence it has on the posterior.
– Computationally expensive with large data sets.
The key takeaway from this discussion is that the prior knowledge can heavily influence the posterior, which can easily be seen in equation (2). That is because knowledge before data collection and the knowledge gained from data collection doesn’t tell the full story unless they are combined.
- Hubbard, D. W. (2010). How to measure anything: Finding the values of “intangibles” in business. (2nd e.d.) New Jersey, John Wiley & Sons, Inc.
- Lewis, D. D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. In European conference on machine learning (pp. 4-15). Springer Berlin Heidelberg.
- Nigam, K., & Ghani, R. (2000). Analyzing the effectiveness and applicability of co-training. In Proceedings of the ninth international conference on Information and knowledge management (pp. 86-93). ACM.
- SAS (n.d.). Bayesian Analysis: Advantages and disadvantages. Retrieved from https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_introbayes_sect006.htm
- Smith, M. (2015). Statistical analysis handbook. Retrieved from http://www.statsref.com/HTML/index.html?introduction.html
- Spiegelhalter, D. & Rice, K. (2009) Bayesian statistics. Retrieved from http://www.scholarpedia.org/article/Bayesian_statistics
- Turhan, B. & Bener, A. (2009). Analysis of Naïve Bayes’ assumptions on software fault data: An emprical study. Retrieved from https://www.researchgate.net/profile/Burak_Turhan/publication/220350237_Analysis_of_Naive_Bayes’_assumptions_on_software_fault_data_An_empirical_study/links/54e798460cf2f7aa4d4dc010.pdf
- Yudkowsky, E.S. (2003). An intuitive explanation of Bayesian reasoning. Retrieved from http://yudkowsky.net/rational/bayes