In:
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, The Royal Society, Vol. 479, No. 2270 ( 2023-02)
Abstract:
Optimal designs minimize the number of experimental runs (samples) needed to accurately estimate model parameters, resulting in algorithms that, for instance, efficiently minimize parameter estimate variance. Governed by knowledge of past observations, adaptive approaches adjust sampling constraints online as model parameter estimates are refined, continually maximizing expected information gained or variance reduced. We apply adaptive Bayesian inference to estimate transition rates of Markov chains, a common class of models for stochastic processes in nature. Unlike most previous studies, our sequential Bayesian optimal design is updated with each observation and can be simply extended beyond two-state models to birth–death processes and multistate models. By iteratively finding the best time to obtain each sample, our adaptive algorithm maximally reduces variance, resulting in lower overall error in ground truth parameter estimates across a wide range of Markov chain parameterizations and conformations.
Type of Medium:
Online Resource
ISSN:
1364-5021
,
1471-2946
DOI:
10.1098/rspa.2022.0453
Language:
English
Publisher:
The Royal Society
Publication Date:
2023
detail.hit.zdb_id:
209241-4
detail.hit.zdb_id:
1460987-3
SSG:
11
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