Prior Knowledge Elicitation: The Past, Present, and Future

Topic

interdisciplinary fields

Frequently Asked Questions (FAQ)

What is prior elicitation and why is it important? Prior elicitation is the process of gathering and converting expert knowledge into well-defined prior probability distributions for use in Bayesian models. This is a crucial step in Bayesian inference because priors encode information about the parameters of interest before observing data, leading to more informative and robust results.
Why is prior elicitation not widely used despite its importance? Despite its importance, prior elicitation is not widely used for several reasons. There’s a lack of user-friendly tools that integrate seamlessly with common probabilistic programming languages. Many existing methods are model-specific, limiting their applicability. Demonstrating the value of prior elicitation in real-world applications has also been limited, leading to less investment and research in the field.
What are the key dimensions that characterize the prior elicitation challenge? Seven key dimensions, forming a ‘prior elicitation hypercube’, characterize the challenges: Properties of the prior: Dimensionality (univariate or multivariate) and type (parametric or nonparametric). Model dependency: Model-specific or model-agnostic methods. Elicitation space: Parameter space or observable space. Interpretation of expert input: Fitting approaches or supra-Bayesian approaches. Computation: Computational complexity of the method. Expert interaction: One-shot or iterative elicitation, modality and assessment tasks. Expert capabilities: Domain knowledge and statistical understanding.
What are the differences between eliciting priors in parameter space and observable space? Eliciting priors in parameter space requires experts to have an understanding of the model parameters and their scales, which might not always be feasible. Elicitation in observable space queries experts about directly measurable quantities (model outcomes). This can be more intuitive for experts, especially when dealing with complex models with numerous uninterpretable parameters.
How can the expert’s input be modelled in prior elicitation? The expert’s input can be modelled using two main approaches: Fitting approaches: Fit a prior distribution to the expert’s responses, often involving minimizing discrepancies or inconsistencies in their judgments. Supra-Bayesian approaches: Treat the expert’s knowledge as data and update the analyst’s prior belief about it using Bayes’ rule. This accounts for inconsistencies using a noise model within the elicitation likelihood.
What is the role of the Bayesian modelling workflow in prior elicitation? A Bayesian workflow for prior elicitation provides a structured and iterative process for incorporating expert knowledge. This includes determining when prior elicitation is necessary, selecting appropriate methods, evaluating the elicited prior, and assessing the sensitivity of the model results to the prior choice.
What are some future directions for improving prior elicitation? Future research should focus on: Developing model-agnostic methods: This allows for wider adoption and integration with existing modelling workflows. Sample-efficient techniques: Minimize expert effort by designing efficient query strategies and utilizing active elicitation. Developing globally joint priors: Simplify elicitation by reducing the number of hyperparameters to elicit. Creating user-friendly software: Integrated with probabilistic programming languages and visualization tools, enabling wider adoption. Rigorous evaluation and benchmark datasets: Allow for standardized comparison of methods and validation of their effectiveness. Showcasing success stories: Highlight the value of prior elicitation in real-world applications to attract funding and encourage adoption.
What is the importance of developing globally joint priors? Globally joint priors can simplify prior elicitation by encompassing most or all model parameters using a smaller set of shared hyperparameters. This reduces the number of parameters to elicit, making the process less cognitively demanding for experts and computationally less intensive.

Significance

Understanding these findings helps advance our knowledge and inform better decisions. This research represents an important contribution to the field. For the full details, watch the video above and explore the linked resources.

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