pymc-devs/pymc

Compute only dependent logp in step samplers

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#7,591 opened on Nov 26, 2024

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Description

Description

As discussed in: https://discourse.pymc.io/t/semantik-description-of-kruschke-diagrams-model-to-graphviz/16142/8 we could be making more use of the conditional partition of the logp graph for partial step samplers.

When the proposal depends on a ratio between probabilities (BinaryMetropolois, BinaryGibbsMetropolis, CategoricalGibbsMetropolis, Slice, NUTS), we don't need to compute terms that don't depend on the updated variables.

Step samples that explicitly request delta_logp (Metropolis, DEMetropolis) achieve this implicitly via PyTensor rewrites.

I suggest adding a model.dependent_logp(vars) that helps us get the logp we need (the variables of interest + conditional dependent variables)

Contributor guide

Research direction
Study the current implementation of logp in PyMC, understand how step samplers compute probabilities, and implement a `dependent logp` method that returns only the logp terms dependent on the specified variables. Follow the discussion link for context.
Tech stack
python
Domain
backend
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
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Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
45