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...

Полное описание

Библиографические подробности
Главные авторы: Chen Cheng, Linjie Wen, Jinglai Li
Формат: Статья
Язык:English
Опубликовано: The Royal Society 2023-08-01
Серии:Royal Society Open Science
Предметы:
Online-ссылка:https://royalsocietypublishing.org/doi/10.1098/rsos.230275