Bayesian statistics

Bayesian analysis fits a statistical model to your data with PyMC and returns a full posterior over the parameters — not a single point estimate, but a distribution with credible intervals — using MCMC, variational inference, or pathfinder sampling. Reach for it when you need uncertainty-aware regression, hierarchical (mixed-effects) models, dose-response curves, or compartmental pharmacokinetic modeling from a dataset you supply. It is one of the specialized predictors that supplements the main property panels. See the Predictors overview for where this fits.

What Bayesian Optimization does

The tool fits one of several model families to a data file you provide (CSV or Parquet) and an outcome/predictor column specification, then samples the posterior with PyMC:

• regression — Bayesian linear (or generalized linear, via link_function) regression over predictor columns.
• hierarchical — multi-level/mixed-effects models, grouped by a group_column.
• time_series — models indexed by a time_column.
• mixture — mixture models over the outcome distribution.
• dose_response — dose-response curve fitting.
• pkpd — pharmacokinetic modeling: one-, two-, or three-compartment models, with iv_bolus, iv_infusion, or oral administration, and an optional population (mixed-effects) mode with per-subject random effects.
• custom — user-supplied PyMC model code.

Inference

• Method: mcmc (default), variational, or pathfinder.
• Likelihood: normal (default), student_t, poisson, binomial, or beta.
• Link function: identity (default), log, or logit.
• Sampling: num_chains (default 4), num_samples (default 2000), num_tune warmup samples (default 1000), target_accept (default 0.9).
• Priors: optional, as {param_name: {distribution: "Normal", params: {mu: 0, sigma: 1}}}.

Typical jobs need no GPU and complete in 2–30 minutes.

Outputs

• A parameter summary (mean, sd, 3%/97% highest-density interval, ESS, R-hat) as CSV.
• The full posterior trace as NetCDF, for further analysis.
• Diagnostics — divergence count, max R-hat, min effective sample size, and (when compute_loo/compute_waic are enabled, both on by default) LOO and WAIC for model comparison.
• Trace, posterior, and posterior-predictive-check plots, when generate_plots is enabled (default).

Chat usage

Example prompts

• "Fit a Bayesian hierarchical model to my dose-response data, grouped by batch"
• "Run a two-compartment PK model on this oral-dose concentration-time data"
• "Fit a Bayesian regression of IC50 against my molecular descriptors and give me credible intervals"

Workflow chaining

• Accepts from: a data file (CSV/Parquet) with your outcome and predictor columns — experimental measurements, PK sampling data, or predictions exported from another tool.
• Feeds into: population PK/PD reporting, decision-making that needs a calibrated uncertainty estimate rather than a point value.

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