n this paper, we show that the performance of a learnt generative model is closelyrelated to the model’s ability to accurately represent the inferredlatent data distri-bution, i.e. its topology and structural properties. We propose LaDDer to achieveaccurate modelling of the latent data distribution in a variational autoencoder frame-work and to facilitate better representation learning. The central idea of LaDDer isa meta-embedding concept, which uses multiple VAE models to learn an embeddingof the embeddings, forming a ladder of encodings. We use a non-parametric mix-ture as the hyper prior for the innermost VAE and learn all the parameters in a uni-fied variational framework. From extensive experiments, we show that our LaDDermodel is able to accurately estimate complex latent distribution and results in improve-ment in the representation quality. We also propose a novel latent space interpolationmethod that utilises the derived data distribution. The code and demos are available athttps://github.com/lin-shuyu/ladder-latent-data-distribution-modelling.