How can I optimize sampling rate in a matrix based Bayesian Model?

For recursive computations that are long enough you’ll probably need to use Scan: scan – Looping in PyTensor — PyTensor dev documentation

A different nuts sampler like numpyro may be needed to stay performant: Faster Sampling with JAX and Numba — PyMC example gallery

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