# A Conditional Density Estimation Partition Model Using Logistic Gaussian Processes

## TL;DR

This paper introduces a novel conditional density estimation method using logistic Gaussian processes within a partition model framework, employing Voronoi tessellations and reversible jump MCMC for efficient inference.

## Contribution

It presents a new approach combining logistic Gaussian processes with Voronoi tessellations and Laplace approximations, enabling effective conditional density estimation with proven consistency.

## Key findings

- Successfully estimates partition structure and conditional distribution in simulations
- Demonstrates effectiveness in real data applications
- Provides a computationally feasible inference algorithm

## Abstract

Conditional density estimation (density regression) estimates the distribution of a response variable y conditional on covariates x. Utilizing a partition model framework, a conditional density estimation method is proposed using logistic Gaussian processes. The partition is created using a Voronoi tessellation and is learned from the data using a reversible jump Markov chain Monte Carlo algorithm. The Markov chain Monte Carlo algorithm is made possible through a Laplace approximation on the latent variables of the logistic Gaussian process model. This approximation marginalizes the parameters in each partition element, allowing an efficient search of the posterior distribution of the tessellation. The method has desirable consistency properties. In simulation and applications, the model successfully estimates the partition structure and conditional distribution of y.