What is 'bayesian priors' in SAP CA-FS-PO - Price Optimization for Banking?

bayesian priors - Overview

• Component: CA-FS-PO

• Component Name: Price Optimization for Banking

• Description: It is necessary to make assumptions about products with sparse historical data based on the behavior of dense, related products. Bayesian priors are the prior or educated estimation of the values of model coefficients.

bayesian priors - Details

• 
  Key Concepts: 
  Bayesian priors are a type of statistical model used in SAP’s CA-FS-PO Price Optimization for Banking component. This model uses prior knowledge of the data to make predictions about future outcomes. It is based on Bayes’ theorem, which states that the probability of an event occurring is equal to the probability of the event given the prior knowledge multiplied by the probability of the prior knowledge itself. 
  
  How to use it: 
  The Bayesian priors model can be used to make predictions about future outcomes in SAP’s CA-FS-PO Price Optimization for Banking component. This model takes into account prior knowledge of the data and uses it to make more accurate predictions. It can be used to identify trends in pricing and optimize pricing strategies for banking products. 
  
  Tips & Tricks: 
  When using Bayesian priors, it is important to ensure that the prior knowledge used is accurate and up-to-date. This will help ensure that the predictions made are as accurate as possible. Additionally, it is important to consider other factors such as market conditions and customer preferences when making predictions with this model. 
  
  Related Information: 
  For more information on Bayesian priors and how they can be used in SAP’s CA-FS-PO Price Optimization for Banking component, please refer to the official SAP documentation. Additionally, there are many online resources available that provide further information on this