6 papers
Tree-Embedded Bayesian Factor Models for Multidimensional Categorical Distributions
Naoki Awaya, Keisuke Sasaki, Genya Kobayashi +1
Analyzing data collected from multiple observational units to estimate common and heterogeneous structures through a hierarchical model is a central task in Bayesian inference, and…
Two-sample comparison through additive tree models for density ratios
Naoki Awaya, Yuliang Xu, Li Ma
The ratio of two densities provides a direct characterization of their differences. We consider the two-sample comparison problem by estimating this ratio given i.i.d. observations…
Generative modeling of density regression through tree flows
Zhuoqun Wang, Naoki Awaya, Li Ma
A common objective in the analysis of tabular data is estimating the conditional distribution (in contrast to only producing predictions) of a set of "outcome" variables given a se…
Unsupervised tree boosting for learning probability distributions
Naoki Awaya, Li Ma
We propose an unsupervised tree boosting algorithm for inferring the underlying sampling distribution of an i.i.d. sample based on fitting additive tree ensembles in a fashion anal…
Hidden Markov Pólya trees for high-dimensional distributions
Naoki Awaya, Li Ma
The Pólya tree (PT) process is a general-purpose Bayesian nonparametric model that has found wide application in a range of inference problems. It has a simple analytic form and th…
Particle rolling MCMC with double-block sampling
Naoki Awaya, Yasuhiro Omori
An efficient simulation-based methodology is proposed for the rolling window estimation of state space models, called particle rolling Markov chain Monte Carlo (MCMC) with double b…