Bayesian inference using qualitative observations of underlying continuous variables.

ABSTRACT:

Motivation

Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a statistical interpretation of the objective functions used in optimization.

Results

We formulated likelihood functions suitable for performing Bayesian UQ using qualitative observations of underlying continuous variables or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for immunoglobulin E (IgE) receptor signaling using synthetic qualitative and quantitative datasets. Remarkably, estimates of parameter values derived from the qualitative data were

SUBMITTER: Mitra ED 

PROVIDER: S-EPMC7214020 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature