Prediction Probe


Bring Your Data to Life with Empirical Process Modeling

A vital step in performing uncertainty analysis or uncertainty-based product design using Probabilistic Technology is to construct process models which accurately and efficiently describe and demonstrate your process. MODELPROBETM, an off-the-shelf, general purpose program constructs these models using available data and innovative statistical methods. By adding MODELPROBETM  to your toolset you will be able to accurately and efficiently estimate unobservable model parameters based upon a measured data set of the observable model variables.

MODELPROBETM  can be used as a standalone tool or in conjunction with general-purpose probabilistic engine UNIPASS®. Key features provide you with the ability to:

  • Import and integrate data from various sources with different formats
  • Filter out undesirable data points
  • Perform numerous mathematical operations on imported data
  • Construct an unlimited number of process models using the same data set
  • Construct regression models for an unlimited number of variables with any order and with multiple cross terms
  • Construct empirical process models for any random process using our proprietary approach
  • Perform comprehensive evaluations of the empirical models using the analysis results, including various graphs, regression statistics, prediction analysis, residual analysis, ANOVA, and much more!


Sample Model