---
title: Bayesian Optimization FAQ
description: >-
  Common questions about model types, sampler settings, PK/PD modeling, and
  convergence diagnostics.
tags:
  - bayesian
  - faq
---

These answers cover which model type to pick, how the sampler settings affect a fit, how PK/PD modeling works, and how to tell whether a fit converged.

## Frequently asked questions

## Which model type should I use?

`regression` for a standard predictor-to-outcome fit, `hierarchical` when your data is grouped (batch, site, subject) and you want to pool information across groups, `dose_response` for a dose-response curve, `time_series` for time-indexed data, `mixture` for a mixture over the outcome distribution, and `pkpd` for compartmental pharmacokinetic modeling. Use `custom` only if none of the built-in model types fit and you can supply your own PyMC model code.

## What does `inference_method` change?

`mcmc` (the default) draws samples with PyMC's NUTS sampler and gives you the most reliable posterior, at the cost of being the slowest option. `variational` fits an approximate posterior faster but can understate uncertainty. `pathfinder` is a faster approximate method that initializes well but, like `variational`, trades some accuracy for speed. Start with `mcmc`; switch only if runtime is a real constraint.

## How many chains and samples do I need?

The defaults — 4 chains, 2,000 samples, 1,000 tuning steps — are a reasonable starting point for most models. More chains make the R-hat convergence check more reliable (you're comparing more independent runs); more samples raise the effective sample size for each parameter. Increase both for a model with many parameters or a complex posterior geometry.

## What if I see divergences?

A nonzero divergence count means MCMC found regions of the posterior geometry it couldn't sample reliably — the resulting posterior may be biased. Raise `target_accept` (toward 0.95–0.99) and increase `num_tune`. Divergences that persist after that usually point to a model reparameterization issue rather than something to tune away.

## How do I fit a population PK model?

Set `model_type: "pkpd"`, choose `pk_model` and `administration`, and provide `time_column`, `concentration_column`, and `dose_column`. Add `subject_column` and set `population_model: true` to fit per-subject random effects (default on `CL` and `V`) on top of the population-level parameters — override `random_effects` to add or change which parameters get subject-level variation.

## What's the difference between LOO and WAIC?

Both are information criteria for comparing model fits on the same data, computed when `compute_loo`/`compute_waic` are enabled (both default on). They usually agree; if they diverge substantially, treat that as a signal to inspect the model rather than picking whichever number looks better.

## Can I supply my own model?

Yes — set `model_type: "custom"` and provide `model_code` with your own PyMC model. This bypasses the built-in model-type logic entirely, so you're responsible for the model's correctness.

## How do I know if the fit converged?

Check `r_hat_max` (want it close to 1.0, conventionally <1.01), `ess_min` (want it reasonably large relative to your total sample count), and `divergences` (want 0). If any of these look off, don't trust the parameter summary yet — increase tuning/sampling or reparameterize first.

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