Prediction Probe


Frequently Asked Questions About Probabilistic Technology, PredictionProbe and UNIPASS® Software



  1. What is Probabilistic Technology?
  2. When was Probabilistic Technology developed?
  3. What is the difference between statistical approaches and Probabilistic Technology?
  4. What is UNIPASS?
  5. Do I have to be an engineer to use UNIPASS?
  6. How long does it take to learn Probabilistic Technology?
  7. How do Monte Carlo simulations relate to Probabilistic Technology?
  8. Why do we need Probabilistic Technology?
  9. Is Probabilistic Technology mature enough for practical applications?
  10. What is the best way to begin implementing the technology?
  11. At what stage of a project should Probabilistic Technology be implemented?
  12. Which industries have potential applications in Probabilistic Technology?
  13. What kind of projects most benefit from Probabilistic Technology?
  14. What kind of operating systems does the UNIPASS software require?
  15. What kind of technology support does PredictionProbe offer its customers?
  16. How do I become a registered user in the Probabilistic Technology Community?

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1. What is Probabilistic Technology?

Probabilistic Technology is one of several predictive technologies aimed at allowing one to predict outcomes. The outcomes could range from how fast a plane can fly, how much a company will earn next year, how reliable a part or system is, what is the most likely failure mode for a system, etc. Sometimes these predictions can be made with statistics, where you operate the systems and gather data on the results. This approach inherently takes into account uncertainties in the variables that drive the outcome, but unless one is careful, there may be unknown biases or other inadequacies in the data. In any case, the statistical method can be very expensive.
Another way of predicting outcomes is by creating a mathematical or rule-based model, both of which are deterministic models. For these, you pick a value for each independent variable and calculate a value for the dependant variable, or outcome. The problem with deterministic models is that they ignore uncertainty, in that the independent variables are usually not single-valued, but are random. We might use safety factors to try to compensate for the uncertainties, but that may either lull us into thinking the safety factors are sufficient, or cause us to over design the system.

The preferred way of predicting outcomes is to use Probabilistic Technology. In Probabilistic Technology, if there is data, we use statistics to characterize the uncertainties in the independent variables. If there isn't any data, we can use engineering judgment to estimate those uncertainties. These random variables then become the input to a deterministic model. Only now the outcome will not be a single-value. Picking from a toolbox full of probabilistic methods, one of the results can be the pdf/cdf curves for the desired outcome. We may also identify the most likely failure conditions. We also will have sensitivity data which will tell us which random variable is critical. If one of the critical variables were evaluated using only engineering judgment, we might decide to create a test to gather data, and then run the analysis again. And this is only some of the benefits of Probabilistic Technology. The bottom line is that Probabilistic Technology has all the benefits of statistics and deterministic models, and yet provides much more information.

PredictionProbe's Probabilistic Technology is a predictive methodology which considers both the physical and statistical aspects of a process while systematically accounting for various types of uncertainties, including inherent uncertainties, model imperfection, lack of data, measurement error, human error, decision uncertainties, human intervention, and much more. PredictionProbe's Probabilistic Technology allows you to: 1) Identify uncertainty accurately and objectively; 2) Systematically incorporate uncertainty into any process; 3) Evaluate the impact of the uncertainty on the outcome; and 4) Rank and identify the critical or sensitive variables or uncertainties which are most important in any process or design.