A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks.
We compare three state-of-the-art Bayesian inference methods for the estimation of the unknown parameters in a stochastic model of a genetic network. In particular, we introduce a stochastic version of the paradigmatic synthetic multicellular clock model proposed by Ullner et al., 2007. By introduci...
Saved in:
| Main Authors: | Inés P Mariño, Alexey Zaikin, Joaquín Míguez |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0182015&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bayesian Computation Methods for Inference in Stochastic Kinetic Models
by: Eugenia Koblents, et al.
Published: (2019-01-01) -
Model Selection and Parameter Estimation for an Improved Approximate Bayesian Computation Sequential Monte Carlo Algorithm
by: Yue Deng, et al.
Published: (2022-01-01) -
Estimation of Stochastic Frontier Models with Fixed Effects through Monte Carlo Maximum Likelihood
by: Grigorios Emvalomatis, et al.
Published: (2011-01-01) -
Advanced Monte Carlo for Acquisition Sampling in Bayesian Optimization
by: Javier Garcia-Barcos, et al.
Published: (2025-01-01) -
Sequential Monte Carlo Squared for online inference in stochastic epidemic models
by: Dhorasso Temfack, et al.
Published: (2025-09-01)