Posterior Ratio Estimation of Latent Variables
This addresses a specific challenge in machine learning for applications involving latent variable inference, but it is incremental as it builds on existing density ratio estimation methods.
The paper tackles the problem of estimating the ratio between two posterior probability density functions of a latent variable, proposing a parametric model-based estimator and proving its consistency and asymptotic normality, with validation through numerical experiments and real-world applications.
Density Ratio Estimation has attracted attention from the machine learning community due to its ability to compare the underlying distributions of two datasets. However, in some applications, we want to compare distributions of random variables that are \emph{inferred} from observations. In this paper, we study the problem of estimating the ratio between two posterior probability density functions of a latent variable. Particularly, we assume the posterior ratio function can be well-approximated by a parametric model, which is then estimated using observed information and prior samples. We prove the consistency of our estimator and the asymptotic normality of the estimated parameters as the number of prior samples tending to infinity. Finally, we validate our theories using numerical experiments and demonstrate the usefulness of the proposed method through some real-world applications.