TY - GEN
T1 - Towards Confident Bayesian Parameter Estimation in Stochastic Chemical Kinetics
AU - Engblom, Stefan
AU - Eriksson, Robin
AU - Vilanova, Pedro
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - We investigate the feasibility of Bayesian parameter inference for chemical reaction networks described in the low copy number regime. Here stochastic models are often favorable implying that the Bayesian approach becomes natural. Our discussion circles around a concrete oscillating system describing a circadian rhythm, and we ask if its parameters can be inferred from observational data. The main challenge is the lack of analytic likelihood and we circumvent this through the use of a synthetic likelihood based on summarizing statistics. We are particularly interested in the robustness and confidence of the inference procedure and therefore estimates a priori as well as a posteriori the information content available in the data. Our all-synthetic experiments are successful but also point out several challenges when it comes to real data sets.
AB - We investigate the feasibility of Bayesian parameter inference for chemical reaction networks described in the low copy number regime. Here stochastic models are often favorable implying that the Bayesian approach becomes natural. Our discussion circles around a concrete oscillating system describing a circadian rhythm, and we ask if its parameters can be inferred from observational data. The main challenge is the lack of analytic likelihood and we circumvent this through the use of a synthetic likelihood based on summarizing statistics. We are particularly interested in the robustness and confidence of the inference procedure and therefore estimates a priori as well as a posteriori the information content available in the data. Our all-synthetic experiments are successful but also point out several challenges when it comes to real data sets.
UR - http://www.scopus.com/inward/record.url?scp=85106448077&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85106448077&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-55874-1_36
DO - 10.1007/978-3-030-55874-1_36
M3 - Conference contribution
AN - SCOPUS:85106448077
SN - 9783030558734
T3 - Lecture Notes in Computational Science and Engineering
SP - 373
EP - 380
BT - Numerical Mathematics and Advanced Applications, ENUMATH 2019 - European Conference
A2 - Vermolen, Fred J.
A2 - Vuik, Cornelis
T2 - European Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2019
Y2 - 30 September 2019 through 4 October 2019
ER -