stan-dev / stan-dev/stan

ADVI diagnostic

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C++
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Description

Summary:

Add PSIS diagnostic for ADVI .

Description:

This paper proposes PSIS diagnostic for ADVI: Yuling Yao, Aki Vehtari, Daniel Simpson, and Andrew Gelman (2018). Yes, but Did It Work?: Evaluating Variational Inference https://arxiv.org/abs/1802.02538

Currently ADVI samples N draws from the approximate distribution and outputs those. In addition lp__ column is inserted, but it has all 0's (it seems stansummary requires lp__ column).

To follow the current design with stansummary, what we propose is

  • after ADVI finishes, compute lp__ and lg__ (log density of of the approximation) in stan/src/stan/variational/advi.hpp

  • output lp__ and lg__ along with parameter sample (for lp__ provide values instead of 0's and add lg__ column). Something needs to be added also in stan/src/stan/services/experimental/advi/meanfield.hpp

  • add algorithms for computing khat and neff in services

  • Given the output we can compute diagnostic afterwards, e.g., using stansummary for CmdStan

  • for simplicity we would implement this first for CmdStan (needs separate issue), ie, modify stansummary to output diagnostics cmdstan/src/cmdstan/stansummary.cpp

Note that initially we start just computing the diagnostics, but eventually we can use diagnostics to improve the algorithm.

Contributor guide

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start with the unchecked work in the issue and read stan/src/stan/variational/advi.hpp, stan/src/stan/services/experimental/advi/meanfield.hpp, and cmdstan/src/cmdstan/stansummary.cpp. Determine how khat and neff should be computed in services and how the resulting diagnostics should be reported; completion is the requested diagnostic output for ADVI, with CmdStan changes handled as described.

Written by the indexing model from the issue text.

Assessment

Tech stack
cpp
Domain
machine-learning, tooling
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
30/100

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