Bayesian inversion for imprecise probabilistic models using a novel entropy-based uncertainty quantification metric

•Bayesian inversion techniques under mixed uncertainty are addressed.•A novel entropy-based uncertainty quantification metric is proposed.•An approximate Bayesian approach is developed for stochastic model updating.•A discretized binning algorithm is employed to reduce the computational cost.•Both static and dynamic case studies are demonstrated for validation.