Cepel - Constructive Probabilistic Learning

Many recent technological breakthroughs are only made possible by probabilistic learning methods. These cover a large span of areas of expertise, e.g. robotics, image recognition or web search algorithms. These techniques combine ideas from machine learning with probability theory.

Our patented solution Cepel is such a probabilistic learning technique. Cepel is an acronym for Constructive Probabilistic Learning. It distinguishes itself from other methods by harnessing in-depth specialized knowledge, thus substantially improving the quality of the results. This new approach to the construction of solutions provides very precise results in many different areas of expertise.

The kernel based estimator of Cepel uses a Set Adaptive Kernel. This way small but important details of the resulting distribution are maintained while the overall curve stays smooth.

The parameter selection employs the Q Function, which itself applies an extended form of cross validation to evaluate the quality of the estimation. A normalization neutralizes linear scalings. The extensive Model Notation and several methods for smart performance improvement complete the technique. 

Cepel vs. Histogram
Cepel automatically generates highly realistic and precise probability estimations. The results show facts that would not be visible otherwise.
The generated estimations support the planning and the evaluation of marketing campaigns. They allow to address specific target groups.
Cepel evaluates performance data in realtime. Outages can be reduced, efficiency increased.
Risk analysis
Cepel calculates risks with high precision. This allows you to make the right decision.