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Covariance

Both components and constraints include covariance, a measure of uncertainty in a Plex. The inclusion of covariance as a core idea is one of the biggest differentiators of MetriCal from competing calibration software.

The Role Of Covariance​

Many calibration pipelines will have some notion of whether or not some parameter is "fixed" or "variable." Fixed parameters are treated as being perfectly observed:

  • Their values are known
  • They have no error
  • They are not optimized during a calibration

Variable parameters are the opposite by being perfectly unobserved:

  • Their values are not known at all
  • They may have any amount of error
  • They are always optimized during a calibration

This creates a dichotomy where we either have zero (0%) information about some quantity, or we have perfect information (100%) about some quantity. This is simply not true; most of the time, there is a reasonable and quantifiable amount of uncertainty in any given system.

As you might have guessed, covariance provides a method of modeling this uncertainty.

Describing Covariance​

While we may not know the exact values of every parameter in our calibration, we can typically make an educated guess. It is common practice to state what we know about a parameter like this:

<parameter> is 1000.0 <units>, ± 0.010 <units>.

This ± 0.010 tolerance gives us a way to initialize a parameter's variance-covariance (shortened to just covariance). Many of MetriCal's processes take this information into account as a statistical prior. Rather than "fixing" any of our quantities, we update these values through the optimization process. This guarantees that we will never converge to values with variance / precision that is worse than what is specified by our priors.

warning

When using covariance, know that this value is standard deviation squared. In our example above, if our standard deviation is ± 0.010 units, then our covariance is (± 0.010)2 units2.

MetriCal incorporates the concept of covariance for all observable quantities in our calibration process. This does add some complexity to the system, but provides the benefit of statistical rigor in our calibration pipeline.