arXiv research

The paper uses Gaussian mixture models for Bayesian networks and proposes an optimization algorithm.

A new method for fast Bayesian mixture model estimation.

The parsimonious Gaussian mixture models, which exploit an eigenvalue decomposition of the group covariance matrices of the Gaussian mixture, have shown their success in particular in cluster analysis. Their estimation is in general performed by maximum likelihood estimation and has also been considered from a parametr…

Bayesian approach learns nonparametric mixture components from heterogeneous data.

Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.

Novel connections between Neyman-Scott processes and Bayesian nonparametric mixture models enable scalable inference.

New method for summarizing Bayesian mixture models using sliced Wasserstein distances.

A new sampler speeds up Bayesian mixture models.

Bayesian models combine experts with a flexible gating mechanism for complex data.

In binary-transaction data-mining, traditional frequent itemset mining often produces results which are not straightforward to interpret. To overcome this problem, probability models are often used to produce more compact and conclusive results, albeit with some loss of accuracy. Bayesian statistics have been widely us…

Paper proposes a VB method for TS-SBP mixture models with reduced computational cost.

New algorithm improves mixing in Bayesian mixture models.

Bayesian model improves cure fraction estimation in survival analysis.

New methods combine model predictions to avoid linear mixtures' limitations.

In this paper, a scale mixture of Normal distributions model is developed for classification and clustering of data having outliers and missing values. The classification method, based on a mixture model, focuses on the introduction of latent variables that gives us the possibility to handle sensitivity of model to out…

Survey on Bayesian inference for Gaussian mixture models.

Bayesian approach approximates probability functions of Gaussian mixtures.

Improved Bayesian analysis for SVM models using a mixture sampler.

Bayesian networks with hidden variables help identify causal relationships obscured by confounding.

Bayesian framework learns latent preference archetypes for many-objective optimization.

Bayesian model averaging improves causal effect estimation by averaging over multiple models.

A new method uses mixture approximations to improve diffusion models for Bayesian inverse problems.

SMI uses mixture models to improve SVGD's performance in Bayesian inference.

Bayesian Optimization improves data mixture selection for large language models.

Bayesian model clusters brain activity time series.

Bayesian framework uses context trees for efficient time series modeling.

Bayesian framework for analyzing heterogeneous covariance data with a novel MoE-Wishart model.

This work relates the framework of model-based clustering for spatial functional data where the data are surfaces. We first introduce a Bayesian spatial spline regression model with mixed-effects (BSSR) for modeling spatial function data. The BSSR model is based on Nodal basis functions for spatial regression and accom…

A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures. In general, the exact solution to this filtering problem involves an exponential growth in the number of mixture terms and this is handled here by utilising a Gaussian mixture reduction step after …

This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture model…

Improved phylogenetic inference using VBPI-Mixtures for tree topology and branch length.

Bayesian mixture models inference in federated learning

HS-MoE selects sparse experts using adaptive priors and data-adaptive gating.

There is a widespread need for statistical methods that can analyze high-dimensional datasets with- out imposing restrictive or opaque modeling assumptions. This paper describes a domain-general data analysis method called CrossCat. CrossCat infers multiple non-overlapping views of the data, each consisting of a subset…

VMoER improves uncertainty quantification in MoE layers for scalable foundation models.

Bayesian models overestimate clusters, but practical summaries can correct this.

We propose the Bayesian bridge estimator for regularized regression and classification. Two key mixture representations for the Bayesian bridge model are developed: (1) a scale mixture of normals with respect to an alpha-stable random variable; and (2) a mixture of Bartlett--Fejer kernels (or triangle densities) with r…

A new method for density estimation using nearest neighbor Dirichlet mixtures.

This work investigates training infinite mixtures with maximum likelihood for improved uncertainty quantification.

We propose a novel "tree-averaging" model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian ensemble trees (BET) and model them as an infinite mixture Dirichlet process. We show that B…

We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes. In sentence comprehension, it is widely assumed that the distance between linguistic co-dependents affects the latency of dependency resolution: the longer the distance, the longer the …

Bayesian approach to robust risk measures under model uncertainty.

A new model predicts multivariate regression using similarities to data points.

We introduce a copula mixture model to perform dependency-seeking clustering when co-occurring samples from different data sources are available. The model takes advantage of the great flexibility offered by the copulas framework to extend mixtures of Canonical Correlation Analysis to multivariate data with arbitrary c…

Bayesian models offer great flexibility for clustering applications---Bayesian nonparametrics can be used for modeling infinite mixtures, and hierarchical Bayesian models can be utilized for sharing clusters across multiple data sets. For the most part, such flexibility is lacking in classical clustering methods such a…

Study on Dirichlet process mixtures for clustering consistency.

New mixture models for clustering and density estimation of unknown distributions.

Enhances RL with function approximation, improving regret bounds.