Two new tests assess how well conditional models fit data.
problem Assessing goodness of fit for conditional distributions.
method Nonparametric statistical tests using Stein operators.
result Tests are consistent and interpretable.
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48 results for “density fitting”
A new kernel Stein test assesses fit for variable-length sequential data.
problem Evaluating goodness of fit for varying-dimensional data like text documents of different lengths.
method Extends kernel Stein discrepancy (KSD) to variable-dimension settings by identifying appropriate Stein operators and proposing a novel KSD goodness-of-fit test.
result The proposed test performs well on discrete sequential data benchmarks.
The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching. Being required to choose a priori a simple kernel such as the Gaussian, however, limits its practical applicability. We provide a scheme for learning a kernel parameterized by …
We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the test determines how likely it is that these were generated from a target density function. The measure of goodness-of-fit is a divergence constructed via Stein's method using functions from a Reproducing Kernel Hilbert Space. O…
In Divide & Recombine (D&R), big data are divided into subsets, each analytic method is applied to subsets, and the outputs are recombined. This enables deep analysis and practical computational performance. An innovate D\&R procedure is proposed to compute likelihood functions of data-model (DM) parameters for big dat…
Riesz regression connects to density ratio estimation for causal inference.
problem Estimating average treatment effects in causal inference.
method Riesz regression as a signed density ratio and least-squares importance fitting.
result Riesz regression and DRE are equivalent, allowing transfer of DRE results.
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Samplingstat.ML
New method estimates model discrepancy without sampling for unnormalized models.
problem Evaluating and training unnormalized density models efficiently.
method Estimate Stein discrepancy using neural network parameterized vector function.
result Method outperforms existing goodness-of-fit tests and training methods.
Bayesian DDR models complex multivariate distributions.
problem Modeling relationships between multivariate distributions with differing dimensions.
method Generalized Bayesian framework using sliced Wasserstein distance and MALA for inference.
result Posterior consistency and robust fits demonstrated in simulations and real data.
Normalizing flows improve density estimation from noisy data.
problem Estimating underlying density from noisy samples.
method Use normalizing flows for density estimation with arbitrary noise distributions, using amortized variational inference.
result Normalizing flows can outperform Gaussian mixtures for density deconvolution.
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…
A new density model using Fourier basis achieves better approximations and compression.
problem Approximating multi-modal 1D densities.
method Constrained Fourier basis model for end-to-end training.
result Lower cross entropy compared to deep factorized models.
A new sequential test for unnormalized densities.
problem Testing unnormalized densities with adaptive stopping.
method Sequential kernelized Stein discrepancy test, using non-uniform Stein kernels.
result Valid test with asymptotic lower bound for growth.
Optimal testing for densities under local differential privacy constraints.
problem Testing goodness-of-fit for densities under privacy constraints.
method Estimation of quadratic distance and minimax separation rates.
result First minimax optimal test under local differential privacy constraints.
New Stein operator improves robustness in model inference.
problem Improving robustness in inference for unnormalized models.
method Density-power weighted Stein operator (-Stein operator).
result Robust methods for goodness-of-fit testing and posterior approximation.
The paper introduces flat-topped PDFs for better fitting machine learning models.
problem Improving goodness of fit in machine learning models.
method Developed a new PDF based on the Fermi-Dirac or logistic function for adaptability.
result Flat-topped PDFs enhance model simplicity and fit quality.
Electronic-structure properties from atom-centered predictions of the electron densityphysics.chem-ph
A new method predicts electron density accurately from atom-centered models.
problem Predicting electron density accurately from atom-centered models.
method Gradient-based approach to minimize loss function in an optimized sparse feature space.
result Extremely accurate predictions of electron density and total energies.
New mixture models for clustering and density estimation of unknown distributions.
problem Clustering and density estimation of data with unknown distributions.
method Two fitting methods: EM algorithm and Bayesian non-parametric method using Gibbs sampler.
result Effective clustering and density estimation of data with unknown distributions.
FORE evaluates occupancy ratios without requiring Bellman completeness.
problem Offline reinforcement learning occupancy ratio estimation.
method Fitted occupancy-ratio evaluation (FORE) using adjoint Bellman recursion.
result FORE achieves convergence in KL without Bellman completeness.
Bayesian approach for multivariate density regression of complex data.
problem Regression of multivariate density-valued responses on predictors.
method Bayesian inference using sliced Wasserstein barycenter and SW distance.
result Accurate fits and reliable predictions for complex data.
New GMM models fit high-dimensional data with fewer parameters.
problem Overparameterization and lack of flexibility in GMMs for high-dimensional data.
method Piecewise-constant covariance eigenvalue profiles, EM and penalized EM algorithms.
result Superior likelihood-parsimony tradeoffs in density fitting, clustering, and denoising.
New method estimates velocity fields for minimizing -divergences without overfitting.
problem Minimizing statistical discrepancies between target and particle distributions.
method Directly estimate velocity fields using interpolation techniques, proving consistency under mild conditions.
result Consistent estimators of velocity fields improve accuracy in applications like domain adaptation and missing data imputation.
Improved estimators for causal inference using cross-fitting and undersmoothing.
problem Estimating expected conditional covariance in causal inference.
method Double cross-fit doubly robust (DCDR) estimators with undersmoothing for non-smooth nuisance functions.
result DCDR estimators achieve -consistency and asymptotic normality under minimal conditions.
Software package assesses spherical data distributions and clusters.
problem Assessing and clustering spherical data distributions.
method Innovative goodness-of-fit tests and clustering algorithms using kernel-based quadratic distances.
result Efficient and mathematically sound goodness-of-fit tests for spherical data.
FlexCodeTS is a flexible time series density estimator.
problem Estimating conditional densities for time series data.
method Nonparametric conditional density estimator based on arbitrary regression methods.
result FlexCodeTS adapts its convergence rate based on the chosen regression method.
We introduce closed-form transition density expansions for multivariate affine jump-diffusion processes. The expansions rely on a general approximation theory which we develop in weighted Hilbert spaces for random variables which possess all polynomial moments. We establish parametric conditions which guarantee existen…
FNFs model parameter-dependent densities by combining a fixed flow with a polynomial parameter-dependent transformation.
problem Learning a separate flow for every parameter configuration is intractable.
method Factorizable Normalizing Flows (FNFs) represent the parameter-dependent density as a fixed flow for a reference configuration and a learnable polynomial transformation factorized over parameters.
result FNFs enable the recovery of the combined effect of multiple parameters without sampling their joint space, providing a scalable and interpretable solution.
A genetic algorithm improves multivariate kernel density estimation.
problem Efficiently estimating multivariate kernel density functions.
method Genetic algorithm applied to subsamples of the original data.
result The genetic algorithm-based estimator performs better than traditional methods.
Generative model for tabular data density regression.
problem Estimating conditional distribution of outcomes given covariates.
method Tree-based flow model for efficient sampling and likelihood evaluation.
result Our method achieves comparable or superior performance with reduced training and sampling costs.
The first step to realize automatic experimental data analysis for fusion plasma experiments is fitting noisy data of temperature and density spatial profiles, which are obtained routinely. However, it has been difficult to construct algorithms that fit all the data without over- and under-fitting. In this paper, we sh…
New method improves Gaussian Mixture Model fitting speed.
problem Slow convergence of EM algorithm for complex data.
method Riemannian Newton Trust-Region method for Gaussian Mixture Models.
result Outperforms existing methods in runtime and iterations.
A study of the personal income distribution in Australiaphysics.soc-ph
We analyze the data on personal income distribution from the Australian Bureau of Statistics. We compare fits of the data to the exponential, log-normal, and gamma distributions. The exponential function gives a good (albeit not perfect) description of 98% of the population in the lower part of the distribution. The lo…
We construct the term structure of the (forward-looking, US market) equity risk premium from SPX option chains. The method is "model-light". Risk-neutral probability densities are estimated by fitting -component Gaussian mixture models to option quotes, where is a small integer (here 4 or 5). These densities are…
A new machine learning model uses score matching to estimate probability densities efficiently.
problem Estimating probability density functions is challenging.
method Introduced a product Jacobi-Theta Boltzmann machine (pJTBM) and used score matching for efficient fitting.
result The pJTBM can fit probability densities more efficiently than the RTBM using score matching.
Graph Mixture Density Networks model multimodal data on graphs.
problem Challenging conditional density estimation problems with structured data.
method Combining mixture models and graph representation learning.
result Significant improvement in likelihood of epidemic outcomes.
Paper uses DRL to improve volatility fitting in equity derivatives.
problem Improving volatility fitting in equity derivatives markets.
method Apply Deep Reinforcement Learning (DRL) to solve the fitting problem.
result DRL algorithms achieve at least as good as standard fitting methods.
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…
New method merges MCMC samples without distributional assumptions.
problem Efficiently merging MCMC samples from disjoint subsets.
method Diffusion generative modelling for density approximation.
result Outperforms existing methods on high-dimensional problems.
Paper models and forecasts intra-day electricity price spreads.
problem Forecasting intra-day price spreads for electricity traders and operators.
method Dynamic density functions based on skewed-t distributions, conditional on exogenous drivers.
result Best fitting and forecasting specifications selected using Pinball Loss function.
The Extreme Deconvolution method fits a probability density to a dataset where each observation has Gaussian noise added with a known sample-specific covariance, originally intended for use with astronomical datasets. The existing fitting method is batch EM, which would not normally be applied to large datasets such as…
ROME improves density estimation for multi-modal, non-normal data.
problem Robust multi-modal density estimation in non-normal, highly correlated distributions.
method ROME uses clustering to segment multi-modal data into uni-modal clusters, then combines KDE estimates for each cluster.
result ROME outperforms state-of-the-art methods and is more robust to various distributions.
Estimates Gaussian mixtures from weighted samples efficiently.
problem Estimating Gaussian mixtures from weighted samples with correct weight treatment.
method Density interpretation and expectation-maximization method considering weights.
result Correctly estimates Gaussian mixtures with weighted samples.
Modeling complex conditional distributions is critical in a variety of settings. Despite a long tradition of research into conditional density estimation, current methods employ either simple parametric forms or are difficult to learn in practice. This paper employs normalising flows as a flexible likelihood model and …
Random forests is a common non-parametric regression technique which performs well for mixed-type data and irrelevant covariates, while being robust to monotonic variable transformations. Existing random forest implementations target regression or classification. We introduce the RFCDE package for fitting random forest…
A new test assesses how well observed networks fit a specified ERGM model.
problem Testing the goodness of fit for ERGMs with a single network observation.
method Kernel Stein discrepancy combined with a discrete Stein operator for ERGMs, Monte Carlo simulation.
result The test provides theoretical and practical support for assessing ERGM fit.
A boosting method improves nonparametric density estimation without smoothing assumptions.
problem Overfitting in nonparametric data fitting.
method Introduces a boosting algorithm for univariate nonparametric maximum likelihood estimation.
result Demonstrates the effectiveness of the boosting approach through simulations and real data experiments.
GGMPs improve non-Gaussian conditional density estimation.
problem Multimodality, heteroscedasticity, and strong non-Gaussianity in conditional density estimation.
method GGMP combines local Gaussian mixture fitting, cross-input component alignment, and per-component heteroscedastic GP training.
result GGMPs improve distributional approximation on synthetic and real-world datasets.
Improves sequence modeling with a flow-based recurrent mixture density network.
problem Sequence modeling and sequence-to-sequence mapping applications.
method Generalized recurrent mixture density networks using normalized flow transformations.
result Significantly improved fit to image sequences measured by log-likelihood.
Hidden Markov models and their variants are the predominant sequential classification method in such domains as speech recognition, bioinformatics and natural language processing. Being generative rather than discriminative models, however, their classification performance is a drawback. In this paper we apply ideas fr…