Parameter estimation from aggregate observations: a Wasserstein distance-based sequential Monte Carlo sampler
In this work, we study systems consisting of a group of moving particles. In such systems, often some important parameters are unknown and have to be estimated from observed data. Such parameter estimation problems can often be solved via a Bayesian inference framework. However, in many practical pr...
| Hlavní autoři: | , , |
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| Médium: | Článek |
| Jazyk: | English |
| Vydáno: |
The Royal Society
2023-08-01
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| Edice: | Royal Society Open Science |
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| On-line přístup: | https://royalsocietypublishing.org/doi/10.1098/rsos.230275 |