arXiv research

Estimates Gaussian mixtures from weighted samples efficiently.

The paper proposes a method for interpretable mixture density estimation using a tree structure.

Paper uses GMM and MAF for probabilistic classification, outperforming simpler models.

Enhances mixture models with classifier-defined weights.

The paper introduces a new method for multivariate density estimation using deep neural mixture models.

The paper analyzes how a known density function can be deviated by a mixture distribution as more data is collected.

A new method for density estimation using mixture discrepancy and moments.

Method estimates joint probability density from samples using low-rank decomposition and random projections.

This paper studies sparse density estimation via $\ell_1$ penalization (SPADES). We focus on estimation in high-dimensional mixture models and nonparametric adaptive density estimation. We show, respectively, that SPADES can recover, with high probability, the unknown components of a mixture of probability densities an…

Graph Mixture Density Networks model multimodal data on graphs.

GGMPs improve non-Gaussian conditional density estimation.

Algorithm estimates nonparametric mixtures from grouped data.

Paper develops a consistent estimator for discrete mixture models.

Normalizing flows improve density estimation from noisy data.

We consider nonparametric estimation of the state price density encapsulated in option prices. Unlike usual density estimation problems, we only observe option prices and their corresponding strike prices rather than samples from the state price density. We propose to model the state price density directly with a nonpa…

New method for private density estimation of high-dimensional Gaussian mixtures.

EnEMF uses Epanechnikov kernel for high-dimensional filtering, improving accuracy and robustness.

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

We introduce a balloon estimator in a generalized expectation-maximization method for estimating all parameters of a Gaussian mixture model given one data sample per mixture component. Instead of limiting explicitly the model size, this regularization strategy yields low-complexity sparse models where the number of eff…

We study the Nonparametric Maximum Likelihood Estimator (NPMLE) for estimating Gaussian location mixture densities in $d$-dimensions from independent observations. Unlike usual likelihood-based methods for fitting mixtures, NPMLEs are based on convex optimization. We prove finite sample results on the Hellinger accurac…

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

A new tensor ring mixture model improves density estimation efficiency.

Study improves density estimation for compact domains using $h$-lifted KL divergence.

Mixture models are powerful statistical models used in many applications ranging from density estimation to clustering and classification. When dealing with mixture models, there are many issues that the experimenter should be aware of and needs to solve. The MixEst toolbox is a powerful and user-friendly package for M…

A new method for estimating density ratios using geodesics on statistical manifolds.

We introduce RNADE, a new model for joint density estimation of real-valued vectors. Our model calculates the density of a datapoint as the product of one-dimensional conditionals modeled using mixture density networks with shared parameters. RNADE learns a distributed representation of the data, while having a tractab…

We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically, within day and from day to day. The second shortcoming is the wide-spread use of Gaussian probability dens…

In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either non-parametrically or using finite mixture models. Thus, clusters are closely related to certain regions around the density modes, and every cluster corr…

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

The thesis evaluates and compares extreme mixture models in finance and insurance.

In this paper we propose a model with a Dirichlet process mixture of gamma densities in the bulk part below threshold and a generalized Pareto density in the tail for extreme value estimation. The proposed model is simple and flexible allowing us posterior density estimation and posterior inference for high quantiles. …

New insights into the top-K sparse softmax gating function for deep learning.

Two new methods improve clustering with missing data.

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

Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.

The two most extended density-based approaches to clustering are surely mixture model clustering and modal clustering. In the mixture model approach, the density is represented as a mixture and clusters are associated to the different mixture components. In modal clustering, clusters are understood as regions of high d…

Proposes a method to partition univariate data into unimodal subsets.

Solves challenges in estimating parameters of softmax gating Gaussian mixture models.

In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classification models. Our basic approach is to sequentially add components to a finite mixture model using the structural expectation maximization (SEM) algorithm. We show that our technique i…

A neural network method estimates densities from characteristic functions.

Normalized compound random measures are flexible nonparametric priors for related distributions. We consider building general nonparametric regression models using normalized compound random measure mixture models. Posterior inference is made using a novel pseudo-marginal Metropolis-Hastings sampler for normalized comp…

The histogram method is a powerful non-parametric approach for estimating the probability density function of a continuous variable. But the construction of a histogram, compared to the parametric approaches, demands a large number of observations to capture the underlying density function. Thus it is not suitable for …

Improved density estimation for mixed discrete-continuous data.

Develops algorithm to differentiate Metropolis-Hastings for optimization.

This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are deter…

Bayesian approach learns nonparametric mixture components from heterogeneous data.

New method improves Gaussian Mixture Model fitting speed.

Low-rank MPPCA improves importance sampling in high dimensions.