fix: conditional GP w/ deterministic delays - #143
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…matic index is passed as arg
Conditioning raised NotImplementedError whenever the model contained a deterministic delay, which blocked conditional GP realizations for any model combining a delay with a Gaussian process. That covers the solar wind case where a deterministic mean density is sampled alongside a GP on the perturbations. With delays present, PulsarLikelihood.y is a CompoundDelay closure rather than an array, and the kernelsolve helpers that conditional calls assumed a static y. Two of the three make_kernelsolve implementations already dispatched to a make_kernelsolve_vary sibling on callable y; this extends the same treatment to the paths conditional uses: - WoodburyKernel_varNP.make_kernelsolve and .make_kernelsolve_simple now evaluate a callable y per parameter set, following the existing pattern for a callable F, and report its parameters. - The ConstantKernel-side make_kernelsolve_simple keeps precomputing F^T N^-1 when y is static and defers only that product when y varies. - conditional drops the restriction and adds the delay's parameters to cond.params. Verified on a solar wind model with a deterministic proxy delay plus a time domain GP: the conditional mean is bit-identical to subtracting the delay from the residuals and conditioning a delay-free likelihood. Adds a regression test covering both constant and varying white noise, checking that the delay parameters reach cond.params, that the result matches the hand-subtracted reference, and that sample_conditional runs. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_013Gj7jjcfUbTrSLDTx3j4QY
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Depends on #141
Claude did most of the heavy lifting here, but it should be straight forward I think.