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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3. What is the difference between statistical approaches and Probabilistic Technology?

Statistics uses past performance data to predict future performance. For example, if one was interested in how long it takes to get to the airport from some office, he or she might collect elapsed time data from people traveling to the airport from the office to determine how long it took them to make the trip. The data could then be fitted to a statistical distribution, and one could use this to predict the probability of getting to the airport in a given time. The emphasis here is on the output time, while the input variables such as route and speed (as well as uncertainties in those variables) are ignored.

In contrast, Probabilistic Technology uses physics or rules to describe the process that yields the desired outcome, and applies statistics to quantify the input (as opposed to output) variables. Thus, in our example, we might collect data on the input variables such as speed and distance, as well as data on other variables such as delays due to accidents, effect of time and date on traffic flow, whether the driver is aggressive or passive, etc. Using that data, we can then create a model to calculate the time to the airport. If we don't have the data available, we can use experts to estimate the missing variable's distribution parameters.

After applying Probabilistic Technology to this problem, the output will be a much more accurate estimate of the probability of reaching the airport before a given time on a given date. In addition, this technique provides the sensitivity of the output variables to changes in the input variables or the distribution parameters for the input variables. We can find out if the output is significantly affected by lack of data or any assumptions we might have made.

The greatest benefits of Probabilistic Technology over statistics are that: (1) predictions are more accurate; (2) it is often easier to obtain data on input variables that output results; and (3) more information is available.