Pathfinder w pytensor symbolic #387
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`fit_pathfinder` - Edited `fit_pathfinder` to produce `pathfinder_state`, `pathfinder_info`, `pathfinder_samples` and `pathfinder_idata` for closer examination of the outputs. - Changed the `num_samples` argument name to `num_draws` to avoid `TypeError` got multiple values for keyword argument 'num_samples'. - Initial points are automatically set to jitter as jitter is required for pathfinder. Extras - New function 'get_jaxified_logp_ravel_inputs' to simplify previous code structure in fit_pathfinder. Tests - Added extra test for pathfinder to test pathfinder_info variables and pathfinder_idata are consistent for a given random seed.
Add a new PyMC-based implementation of Pathfinder VI that uses PyTensor operations which provides support for both PyMC and BlackJAX backends in fit_pathfinder.
Add a PyMC/PyTensor implementation of Pathfinder VI as an alternative to the existing BlackJAX backend. Key changes include: - Implement core Pathfinder components using PyTensor with batched operations - Add inference_backend parameter to select between PyMC and BlackJAX implementations - Enable jittering of initial points for Pathfinder
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Another version to draft PR #386 which uses more of PyTensor's symbolic variables and compiling functions.
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