Introduce NonNegativeReal parameters

[jamesbriant]

Checklist

• I've formatted the new code by running hatch run dev:format before committing.
• I've added tests for new code.
• I've added docstrings for the new code. N/A

Description

Introduced a new NonNegativeReal parameter type. This has two primary applications.

Kernel variance parameter

This has been implemented into kernel variance parameters. This allows kernels to be 'turned off' as required by setting variance=gpx.parameter.Static(0.0). This is potentially useful for kernel searching algorithms.

I have been using this feature as an optional add-on kernel which defaults to 'off'.

Gaussian likelihood obs_stddev

By allowing gpx.likelihoods.Gaussian() to accept obs_stddev=0.0 we can 'turn off' the likelihood. This is important for GP emulators of computer models. The easiest way to see this working well is by removing the observation noise from the regression example on the documentation.

There is perhaps a cleaner solution by defining a new likelihood class, emulator say, with no obs_stddev argument which simply returns the required number of zeros when called.

Why is this needed at all?

One could argue that this is not necessary as using gps.Prior() should be sufficient for building emulators. From a development point of view, it is easier to build tools that rely purely on the AbstractPosterior class with a likelihood of fixed structure baked in rather than also having to account for the Prior class thus rendering a likelihood 'optional'. Hence a quick 'hack' such as my implementation, or potentially an arguably cleaner emulator likelihood is easier to account for.

Issue Number: N/A

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[thomaspinder]

Thanks @jamesbriant - let me begin taking a look