by Dax Bradley
The Bayesian model for statistical analysis is highly popular in settings that require predictive strength to succeed. Industries are numerous, from academic areas such as astronomy, to industrial applications to include telecommunications and pharmaceutical companies (May, 2014). In the telecom sector, the method has been employed for some time. Akamai Technologies in Cambridge uses Bayesian methods to better understand network latencies, internet traffic, and to identify clusters of users (Galkowski, 2014).
The computational complexity of the Bayesian model would suggest that primarily technology-based efforts would embrace this form of analysis (Edelsbrunner, 2014). Google’s robotic cars, and Microsoft’s own early spam filters stood on the strength of Bayes theorem (McGrayne, 2011). Google’s strength lies not only in the high level of technical prowess, which is obvious, but also by a factor not apparent: the large amount of data to draw from.
The Bayes approach relies heavily on what is known already to make a successful prediction on what is likely to happen (Russell & Norvig, 2010). Google translate uses Bayes Theorem in the translation services, by drawing on a large pool of translation data already completed by humans (King, 2015). So, rather than undergoing large amounts of translation algorithms, the software instead draws from experience, samples the data, and predicts the highest certainty. The method has been applied by Google not only in translation and mapping, but also in controlling driverless cars.
The tech giant claims that their autonomous vehicles have logged as many as 140,000 miles along the Pacific Coastal highway, all without incident (Matyszczyk, 2011).
It is easy to see how Bayesian methods are highly prized in the technology sector. The approach is also critical in health technology assessment. While no formal definition of Bayesian tools in healthcare technology has been established, proponents believe that it provides for conclusions in a form most appropriate for decisions specific to patients and decisions affecting policy. That is based upon the following conclusions (Spiegelhalter, Myles, & Jones, 1999, para. 4):
- Data are interpreted from a study in the light of external evidence and judgement
- The form in which conclusions are drawn naturally contributes to decisions
- Prior plausibility of hypotheses is taken into account
- Skepticism about large treatment effects are formally expressed and cautiously interpreted
- Use of Bayesian methods in healthcare technology assessment are to be pursued carefully; guidelines, software, and critically evaluated case studies are needed
References
Galkowski, J. (2014, October 17). How companies use Bayesian methods [Online forum comment]. Retrieved from http://andrewgelman.com/2014/10/17/companies-use-bayesian-methods/
King, M. (2015, July 20). Bayes’ theorem and what we do [Blog post]. Retrieved from https://www.whitehatsec.com/blog/bayes-theorem-and-what-we-do/
Matyszczyk, C. (2011, March 4). Google’s self-driving car goes all Dale Earnhardt. Cnet. Retrieved from https://www.cnet.com/news/googles-self-driving-car-goes-all-dale-earnhardt/
May, J. (2014, October 17). How do companies use Bayesian methods? [Online forum comment]. Retrieved from http://andrewgelman.com/2014/10/17/companies-use-bayesian-methods/
McGrayne, S. (2011). Why Bayes rules: The history of a formula that drives modern life. Retrieved January 19, 2017, from https://www.scientificamerican.com/article/why-bayes-rules/
Russell, S., & Norvig, P. (2010). Artificial Intelligence: A modern approach (3rd ed.). Boston, MA: Pearson.
Spiegelhalter, D., Myles, J., & Jones, D. (1999, August 21). An introduction to bayesian methods in health technology assessment. The BMJ, 319(1), 7208. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1116393/
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