The Beauty of the Bayes’ Theorem — The Trending of Bayesian Analysis
The Bayes’ theorem is de rigueur these days across the scientific community in areas ranging from cognitive science, biomedical research, and quantum mechanics (multiverse and string theories) to molecular analysis.
The Bayes’ theorem, named after the Reverend Thomas Bayes (1701–1761), is a mathematical model that provides a framework of understanding the world around us. Bayesian statistics, when deployed properly, has commercial value in solving complex problems across diverse disciplines ranging from genomics to self-driving automobiles.
Bayesian models are being used on genomic data sets to understand if computation modeling can be used to differentiate between lung cancer subtypes, regardless if the tissue sampled is the actual tumor or adjacent tissue [1].
James Kozloski, a prolific inventor at IBM, has filed for a patent for a cognitive assistant that uses a combination of techniques including Bayesian inference, machine learning and monitoring [2].
Google’s driverless cars use a Bayesian model, similar to a hidden Markov model, in order to indirectly determine the vehicle’s location within its environment (localization).
Even researchers in paleontology are beginning to deploy Bayesian analysis on data sets [3].
Using the Bayes’ theorem is a trend that is expected to rise across all fields of science given the recent historic statement by the American Statistical Association (ASA) regarding the use of P-Values [4].
Mankind is so close, yet so far from cracking the many mysteries of science. The attempt to bring order from the chaos of complexity via statistical models and mathematics is an arduous undertaking and yet so noble an endeavor. Therein lies the beauty of the Bayes’ theorem.
Copyright © 2016 Cami Rosso All rights reserved.
References
- Pineda et al., “On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue.” BMC Cancer, 4 March 2016.
- Kozloski, James. US Patent Application №13/599,359, 2012.
- Tennant, J.. “The Evolution of tyrannosaurs.” PLOSblogs, February 2016.
- Rosso, Cami. “Fixing the Problem of P-Values in Scientific Research.”LinkedIn, 2016.
Originally published at https://www.linkedin.com on March 31, 2016.
