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    Ivy Biosciences Docs/Tools/Predictors/Bayesian/Bayesian Optimization quickstart

    Bayesian Optimization quickstart

    Point at a CSV, pick regression, hierarchical, dose_response or pkpd, set the outcome and predictors, then read r_hat and divergences before the posterior.

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    bayesianquickstart

    You have a dataset and want parameter estimates with honest uncertainty rather than a single point value. This walks through fitting a model in a few steps: point at your data, pick a model type, set the columns, submit, and read the posterior.

    Quick model fit with .bloom* platform

    The tool fits your chosen model with PyMC, sampling the posterior by MCMC (default), variational inference, or pathfinder, and returns a parameter summary with credible intervals plus convergence diagnostics.

    Step-by-step workflow

    Point at your data

    Provide data_url — a CSV or Parquet file reachable by the job. Include the outcome column and every predictor column you want in the model.

    Choose a model type

    • regression — for a standard Bayesian linear/generalized-linear fit. Pick link_function: "log" or "logit" for a GLM.
    • hierarchical — add group_column to pool information across groups (e.g. batch, site, or animal).
    • dose_response — for a dose-response curve.
    • pkpd — set pk_model (one_compartment / two_compartment / three_compartment), administration (iv_bolus / iv_infusion / oral), and time_column / concentration_column / dose_column. Add subject_column and population_model: true for a population (mixed-effects) PK fit.
    • time_series or mixture — set time_column, or leave defaults for a mixture over the outcome.
    • custom — supply your own model_code (PyMC).

    Set the outcome and predictors

    • outcome_column: the dependent variable.
    • predictor_columns: one or more predictor columns.
    • Leave likelihood at normal unless your outcome is a count (poisson), binary (binomial), or a proportion (beta).

    Leave the sampler at defaults, or tune it

    Defaults (num_chains: 4, num_samples: 2000, num_tune: 1000, target_accept: 0.9) work for most models. Raise target_accept toward 0.99 if the diagnostics later report divergences.

    Submit and wait

    Jobs run without a GPU and typically complete in 2–30 minutes depending on model complexity and chain length.

    Read the results

    • Open posterior_summary_csv and check r_hat is close to 1.0 and divergences is 0 before trusting any parameter's mean/HDI.
    • For a PK/PD fit, read the compartmental parameters (e.g. CL, V) the same way.
    • Use loo/waic from diagnostics_json to compare against a second model fit on the same data, if you ran more than one.

    Common scenarios

    Dose-response fit

    1. Set model_type: "dose_response", outcome_column to the measured response, predictor_columns to the dose.
    2. Submit and read the fitted curve parameters and their credible intervals.

    Population PK model

    1. Set model_type: "pkpd", pk_model: "two_compartment", administration: "oral".
    2. Set time_column, concentration_column, dose_column, and subject_column.
    3. Set population_model: true and, if you want random effects on parameters other than clearance and volume, override random_effects.

    Hierarchical regression

    1. Set model_type: "hierarchical", group_column to the grouping variable.
    2. Read the group-level and population-level parameter rows separately in the summary.

    Bayesian Optimization inputs and parameters

    Data format, model types, priors, and sampler settings.

    Bayesian Optimization on the platform

    Bayesian statistical modeling and PK/PD analysis with PyMC.

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    Bayesian Optimization outputs and interpretation

    Parameter posteriors, HDI intervals, diagnostics, and model comparison metrics from a Bayesian fit.

    Bayesian Optimization troubleshooting

    Clear divergences by raising target_accept, read an r_hat_max above 1.01 or a low ess_min, and fix a PK fit whose pk_model or administration is wrong.

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