Chen, Ding-Geng (Din)Gao, HaipengJi, Chuanshu2022-09-152022-09-152021-12-15Chen, D.-G.; Gao, H.; Ji, C. Bayesian Inference for Stochastic Cusp Catastrophe Model with Partially Observed Data. Mathematics 2021, 9, 3245. https://DOI.org/10.3390/math9243245.2227-739010.3390/math9243245https://repository.up.ac.za/handle/2263/87205The purpose of this paper is to develop a data augmentation technique for statistical inference concerning stochastic cusp catastrophe model subject to missing data and partially observed observations. We propose a Bayesian inference solution that naturally treats missing observations as parameters and we validate this novel approach by conducting a series of Monte Carlo simulation studies assuming the cusp catastrophe model as the underlying model. We demonstrate that this Bayesian data augmentation technique can recover and estimate the underlying parameters from the stochastic cusp catastrophe model.en© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Cusp catastrophe modelStochastic differential equationTransition densityBayesian inferenceData augmentationHamiltonian Monte CarloArticle