DeepAI AI Chat
Log In Sign Up

Prior knowledge elicitation: The past, present, and future

by   Petrus Mikkola, et al.
Helsingin yliopisto

Specification of the prior distribution for a Bayesian model is a central part of the Bayesian workflow for data analysis, but it is often difficult even for statistical experts. Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions, and offers a solution to the prior specification problem, in principle. In practice, however, we are still fairly far from having usable prior elicitation tools that could significantly influence the way we build probabilistic models in academia and industry. We lack elicitation methods that integrate well into the Bayesian workflow and perform elicitation efficiently in terms of costs of time and effort. We even lack a comprehensive theoretical framework for understanding different facets of the prior elicitation problem. Why are we not widely using prior elicitation? We analyze the state of the art by identifying a range of key aspects of prior knowledge elicitation, from properties of the modelling task and the nature of the priors to the form of interaction with the expert. The existing prior elicitation literature is reviewed and categorized in these terms. This allows recognizing under-studied directions in prior elicitation research, finally leading to a proposal of several new avenues to improve prior elicitation methodology.


page 1

page 2

page 3

page 4


Flexible Prior Elicitation via the Prior Predictive Distribution

The prior distribution for the unknown model parameters plays a crucial ...

Bayesian Workflow

The Bayesian approach to data analysis provides a powerful way to handle...

Centered Partition Process: Informative Priors for Clustering

There is a very rich literature proposing Bayesian approaches for cluste...

Dynamic transformation of prior knowledge intoBayesian models for data streams

We consider how to effectively use prior knowledge when learning a Bayes...

Credal Model Averaging for classification: representing prior ignorance and expert opinions

Bayesian model averaging (BMA) is the state of the art approach for over...

Toward a principled Bayesian workflow in cognitive science

Experiments in research on memory, language, and in other areas of cogni...