Name
Title | ||
|---|---|---|
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 nearly as consistent with the assumed ground-truth parameter values as estimates derived from the lower throughput quantitative data. These results provide further motivation for leveraging qualitative data in biological modeling. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1093/bioinformatics/btaa084 | BIOINFORMATICS |
DocType | Volume | Issue |
Journal | 36 | 10 |
ISSN | Citations | PageRank |
1367-4803 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eshan D Mitra | 1 | 0 | 0.34 |
| William S. Hlavacek | 2 | 277 | 24.15 |