# Mixed Likelihood Gaussian Process Latent Variable Model

arXiv: https://arxiv.org/abs/1811.07627 · HTML: https://mlsanity.com/paper/1811.07627/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference](https://mlsanity.com/paper/2202.12979/) (2022)
- [Mixed Likelihood Variational Gaussian Processes](https://mlsanity.com/paper/2503.04138/) (2025)
- [Differentiable latent structure discovery for interpretable forecasting in clinical time series](https://mlsanity.com/paper/2604.27967/) (2026)

## Abstract (arXiv)

We present the Mixed Likelihood Gaussian process latent variable model (GP-LVM), capable of modeling data with attributes of different types. The standard formulation of GP-LVM assumes that each observation is drawn from a Gaussian distribution, which makes the model unsuited for data with e.g. categorical or nominal attributes. Our model, for which we use a sampling based variational inference, instead assumes a separate likelihood for each observed dimension. This formulation results in more meaningful latent representations, and give better predictive performance for real world data with dimensions of different types.