As a data science professional, you likely hear the word “Bayesian” a lot. Whether it’s Bayesian Inference, Bayesian Statistics, or other Bayesian methods, knowing how to use and apply these methods is almost a necessity for any practicing professional. There are many different applications that one may use, and through a variety of industries. As such, here are a few examples of when you might use Bayesian methods.
Business and Commerce
Bayesian Inference is a popular data science technique for businesses, especially for pricing decisions. Businesses can determine prices for products based on field information like retail and wholesale prices, the size of the market, and market share.
Bayesian methods can also be used for new product development as a whole. Mainly, one would look at project risk by weighing uncertainties and determining if the project is worth it.
However, when it comes to Bayesian inference and business decisions, the most common application relates to product ranking. Wayfair developed its own Bayesian system to help customers have an ideal and customized shopping experience. Online shopping giants like Amazon use it to make ratings appear natural when searching for products, as opposed to displaying rankings in order the default option.
Marketing decisions come in a number of ways. Some teams throw ideas at a dartboard and hope that one sticks. Others will use Bayesian methods to look at previous campaigns and marketing information to improve existing campaigns or to make new ones. You can also use Bayesian approaches in A/B testing to play around with email strategies, website designs, and so on. Market research also benefits from a Bayesian approach by examining probably insights to target the right audience.
In a field all about predictions based on trends, Bayesian networks can be used to identify future trends in stocks based on previous trends. This has been a popular trend over the past year, especially as COVID was a new variable for investors to be concerned with.
While there’s nothing wrong with traditional forecasting methods, Bayesian deep learning can be used to predict the weather too. The research is still in its early stages, but some researchers are aiming to use recent computer vision imagery and recent weather patterns to predict what will happen next.
Bayesian methods can be used in disease mapping to determine underlying disease risk in individuals and groups. With Bayesian hierarchical models, researchers can look at individual risk factors, subregions with risks and predispositions to disease, and other variables to determine if one may succumb to a particular disease.
Most doctors use Bayesian inference without realizing it. They get a sick patient, look at their history, their lifestyle, and other factors to determine what problem the patient may have. Bayesian analysis can even be used to fill in incomplete medical records based on the history and trends of the individual.
Learn more about Bayesian methods with Ai+
This August 17th in our upcoming Ai+ Training session, Bayesian Inference with PyMC, Allen Downey, Professor of Computer Science at Olin College, will provide a 4-hour hands-on training session that introduces PyMC and shows how to apply Bayesian inference in real-world settings. If you register by July 30th, you can save 30% on your ticket and learn this in-demand skill this summer.