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In this work we study systems consisting of a group of moving particles. Insuch systems, often some important parameters are unknown and have to beestimated from observed data. Such parameter estimation problems can often besolved via a Bayesian inference framework. However in many practical problems,only data at the aggregate level is available and as a result the likelihoodfunction is not available, which poses challenge for Bayesian methods. Inparticular, we consider the situation where the distributions of the particlesare observed. We propose a Wasserstein distance based sequential Monte Carlosampler to solve the problem: the Wasserstein distance is used to measure thesimilarity between the observed and the simulated particle distributions andthe sequential Monte Carlo samplers is used to deal with the sequentiallyavailable observations. Two real-world examples are provided to demonstrate theperformance of the proposed method.

Parameter estimation from aggregate observations: A Wasserstein distance based sequential Monte Carlo sampler