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elicito: A Python package for expert prior elicitation#

elicito supports the implementation of expert prior elicitation methods. The goal of expert prior elicitation is to specify prior distributions for parameters in a Bayesian model that accurately reflect the expectations of a domain expert.

With its modulare framework elicito supports

  • a wide range of generative models (statistical model describing the data-generating process),
  • different types of expert knowledge (which information is extracted from the domain expert?)
  • various types of elicitation techniques (how the information is extracted from the domain expert)
  • loss functions (criterion used to match expert knowledge with simulated model quantities)

Conceptual background#

Given a generative model and a set of initial hyperparameters defining the prior distributions, the model can be run in forward mode to simulate elicited summaries by computing the predefined target quantities and summary statistics. These simulated summaries are then compared with the expert-elicited summaries obtained during the expert-elicitation stage. An iterative optimization scheme is employed to update the hyperparameters of the parametric prior distributions so as to minimize the discrepancy between simulated and expert-elicited summaries. In other words, the objective is to identify the vector of hyperparameters that yields the closest alignment between simulated and expert-elicited summaries.

conceptual workflow

The core logic of the expert prior elicitation method proposed in Bockting et al. (2024) can be summarized in a five-step workflow:

Core logic of method underlying elicito

  1. Define the generative model: Specify the generative model, including the functional form of the data distribution and the parametric family of prior distributions.
  2. Define target quantities and elicitation techniques: Select the set of target quantities and determine the elicitation techniques to query the expert (cf. elicited summaries).
  3. Simulate elicited summaries: Draw samples from the generative model and compute the corresponding set of simulated elicited summaries.
  4. Evaluate discrepancy between simulated and expert-elicited summaries: Assess the discrepancy between the simulated and expert-elicited summaries using a multi-objective loss function.
  5. Adjust prior hyperparameters to minimize discrepancy: Apply an optimization scheme to update the prior hyperparameters such that the loss function is minimized.

Main References#

  • [Software Paper] Bockting F. & Bürkner PC (2025). elicito: A Python package for expert-prior elicitation. arXiv. Preprint
  • [Methods Paper] Bockting F., Radev ST, Bürkner PC (2024). Simulation-based prior knowledge elicitation for parametric Bayesian models. Scientific Report, 14(1), 17330. PDF
  • [Methods Paper] Bockting F., Radev ST, Bürkner PC (2025). Expert-elicitation method for non-parametric joint priors using normalizing flows. Statistics and Computing. PDF

Implementation (in a nutshell)#

The primary user interface of elicito is the Elicit class, through which the user can specify the entire elicitation procedure. The arguments of the Elicit class are designed to capture all necessary information required to implement an elicitation method. A brief overview of these arguments is provided below:

  • model: Defines the generative model used in the elicitation procedure.
  • parameters: Specifies assumptions regarding the prior distributions over model parameters, including (hyper)parameter constraints, dimensionality, and parametric form.
  • targets: Defines the elicited statistics in terms of target quantities and corresponding elicitation techniques. Also specifies the discrepancy measure and weight used for the associated loss component.
  • expert: Provides the expert information that serves as the basis for the learning criterion.
  • optimizer: Specifies the optimization algorithm to be used, along with its hyperparameters (e.g., learning rate).
  • trainer: Configures the overall training procedure, including settings such as the random seed, number of epochs, sample size, and batch size.
  • initializer: Defines the initialization strategy for the hyperparameters used to instantiate the simulation-based optimization process.
  • networks: Specifies the architecture of the deep generative model; required only when using non-parametric prior distributions.

By configuring these core components, elicito supports a wide range of elicitation methods, including both structural and predictive approaches, univariate and multivariate as well as parametric and nonparametric prior distributions.

Getting started#

  • See introductory tutorials for learning

    • independent, parametric prior distributions Notebook
    • joint, non-parametric prior distributions Notebook

  • Browse through the

    • package API to get familiar with the functionalities
    • How-to-guides to see further use cases and explanations

Please don't hesitate to raise an issue if you get stuck, encounter an error, or have a question. We are happy to hear from you.