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This paper presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models…

Machine Learning · Computer Science 2024-01-22 Hengrui Luo , Younghyun Cho , James W. Demmel , Xiaoye S. Li , Yang Liu

We present a new version of the truncated harmonic mean estimator (THAMES) for univariate or multivariate mixture models. The estimator computes the marginal likelihood from Markov chain Monte Carlo (MCMC) samples, is consistent,…

Flexible regression methods where interest centres on the way that the whole distribution of a response vector changes with covariates are very useful in some applications. A recently developed technique in this regard uses the…

Computation · Statistics 2016-03-01 Meng Hwee Victor Ong , David J. Nott , Ajay Jasra

To improve the predictability of complex computational models in the experimentally-unknown domains, we propose a Bayesian statistical machine learning framework utilizing the Dirichlet distribution that combines results of several…

Methodology · Statistics 2023-11-06 Vojtech Kejzlar , Léo Neufcourt , Witold Nazarewicz

Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum…

Computation · Statistics 2015-09-25 Hua Zhou , Liuyi Hu , Jin Zhou , Kenneth Lange

Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog. They are useful for a number of machine learning tasks, including product…

Machine Learning · Statistics 2016-08-17 Mike Gartrell , Ulrich Paquet , Noam Koenigstein

Marked point process data arise when events occur in a space with event-level marks. We study clustering of replicated marked Poisson point processes and introduce Dirichlet process mixtures of marked Poisson point processes, a Bayesian…

Methodology · Statistics 2026-05-12 Minsung Choi , Seonghyun Jeong

Completely random measures provide a principled approach to creating flexible unsupervised models, where the number of latent features is infinite and the number of features that influence the data grows with the size of the data set. Due…

Machine Learning · Statistics 2020-06-26 Peiyuan Zhu , Alexandre Bouchard-Côté , Trevor Campbell

We propose a general modeling framework for marked Poisson processes observed over time or space. The modeling approach exploits the connection of the nonhomogeneous Poisson process intensity with a density function. Nonparametric Dirichlet…

Methodology · Statistics 2011-11-02 Matthew A. Taddy , Athanasios Kottas

This article is motivated by challenges in conducting Bayesian inferences on unknown discrete distributions, with a particular focus on count data. To avoid the computational disadvantages of traditional mixture models, we develop a novel…

Methodology · Statistics 2025-11-12 Davide Agnoletto , Tommaso Rigon , David B. Dunson

We introduce a new class of multilevel, adaptive, dual-space methods for computing fast convolutional transforms. These methods can be applied to a broad class of kernels, from the Green's functions for classical partial differential…

Numerical Analysis · Mathematics 2023-09-12 Shidong Jiang , Leslie Greengard

Dynamic factor models are often estimated by point-estimation methods, disregarding parameter uncertainty. We propose a method accounting for parameter uncertainty by means of posterior approximation, using variational inference. Our…

Methodology · Statistics 2022-10-14 Erik Spånberg

Driven by the critical needs of biomanufacturing 4.0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of bioprocess mechanisms. It can faithfully capture the important…

Machine Learning · Statistics 2022-10-03 Wei Xie , Keqi Wang , Hua Zheng , Ben Feng

In this article, we propose a new method for the fundamental task of testing for dependence between two groups of variables. The response densities under the null hypothesis of independence and the alternative hypothesis of dependence are…

Methodology · Statistics 2015-01-29 Yimin Kao , Brian J Reich , Howard D Bondell

This paper develops an asymptotic theory for two-step debiased machine learning (DML) estimators in generalised method of moments (GMM) models with general multiway clustered dependence, without relying on cross-fitting. While cross-fitting…

Econometrics · Economics 2026-04-07 Kaicheng Chen , Harold D. Chiang

Ensemble methods are commonly used in classification due to their remarkable performance. Achieving high accuracy in a data stream environment is a challenging task considering disruptive changes in the data distribution, also known as…

Machine Learning · Computer Science 2023-09-07 Soheil Abadifard , Sepehr Bakhshi , Sanaz Gheibuni , Fazli Can

To reduce complexity and achieve scalable performance in high-dimensional black-box settings, we propose a distributed method for nonconvex derivative-free optimization of continuous variables with an additively separable objective, subject…

Optimization and Control · Mathematics 2025-11-03 Damilola Fasiku , Wentao Tang

We propose a hybrid estimation procedure to estimate global fixed parameters and subject-specific random effects in a mixed fractional Black-Scholes model based on discrete-time observations. Specifically, we consider $N$ independent…

Statistics Theory · Mathematics 2026-02-13 Nesrine Chebli , Hamdi Fathallah , Yousri Slaoui

Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to modeling the dynamics of a latent state, which is observed at discrete-time points via a likelihood model. However, inference in GPSSMs is…

Machine Learning · Computer Science 2023-07-18 Xuhui Fan , Edwin V. Bonilla , Terence J. O'Kane , Scott A. Sisson

Using Kalman techniques, it is possible to perform optimal estimation in linear Gaussian state-space models. We address here the case where the noise probability density functions are of unknown functional form. A flexible Bayesian…

Statistics Theory · Mathematics 2009-11-13 François Caron , Manuel Davy , Arnaud Doucet , Emmanuel Duflos , Philippe Vanheeghe