Proposes evaluating models through posterior dispersion indices.
problem The need for a better model evaluation metric beyond predictive accuracy.
method Introduces posterior dispersion indices (PDI) to evaluate probabilistic models.
result Identifies rich patterns of model mismatch in various real data examples.
A simple pooling technique prevents posterior collapse in sequence VAEs.
problem Posterior collapse in sequence VAEs, causing models to ignore latent variables.
method Proposed a pooling technique to prevent posterior collapse.
result Significantly better data log-likelihood achieved compared to standard sequence VAEs.
A framework connects VAEs to GLMs for better model initialization and performance.
problem Understanding and optimizing loss function critical points in VAEs.
method Introducing a theoretical framework based on GLM and EDFs.
result Maximum likelihood initialization improves VAE performance.
VB under misspecified models yields asymptotically normal posterior and predictive distributions.
problem Theoretical guarantees for VB under model misspecification.
method Proved asymptotic normality and KL divergence minimization for VB posterior under misspecified models.
result VB posterior mean centers at the true distribution's minimum KL divergence, explaining predictive accuracy with MCMC.
Skewness dispersion predicts future stock market returns, especially in months with monetary policy announcements.
problem Predicting future stock market returns using skewness dispersion.
method Cross-sectional analysis of firm-level realized skewness and stock market returns.
result Skewness dispersion is a significant predictor of future stock market returns, robust to various estimation methods.
New dispersion indices based on inaccuracy and divergence introduced for information measures.
problem Measuring variability in uncertainty measures.
method Introducing new dispersion indices based on Kerridge inaccuracy and Kullback-Leibler divergence.
result Properties, bounds, and examples of new dispersion indices presented.
Bayesian geoacoustic inversion improved using MDN.
problem Efficiently solving Bayesian geoacoustic inversion problems.
method Deriving geoacoustic statistics from multidimensional posterior density using MDN, training the network on the whole parameter space.
result The network provides reliable predictions and good generalization performance, solving problems in seconds.
New heat dispersion laws established for smooth compact manifolds.
problem Understanding heat dispersion in smooth compact manifolds.
method Established new heat dispersion laws through Theorem 1.1 and explored them further with Propositions 3.1 and 3.2.
result New heat dispersion laws for smooth compact manifolds.
Paper predicts urban dispersal events using deep survival analysis on mobility data.
problem Predicting abnormal dispersal events in urban areas to mitigate congestion and safety risks.
method Formulated as a survival analysis problem, developed a two-stage deep learning framework (DILSA).
result DILSA predicts dispersal events with F1-score of 0.7 and average time error of 18 minutes.
Bayesian framework for semiparametric regression of discrete data.
problem Complex distributional features of discrete data.
method Semiparametric modeling with nonparametric marginal and latent linear regression.
result Posterior consistency and analytical/posterior predictive distributions.
MallowsPO enhances LLM fine-tuning with a dispersion index of human preferences.
problem Lack of diversity in human preferences in DPO.
method Developed a dispersion index based on Mallows' theory to characterize preference diversity.
result Demonstrated improved performance in various tasks using the dispersion index.
Geometric focusing affects dispersive estimates for Schrödinger and wave equations.
problem Long-time decay rate in dispersive estimates for Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones.
method Classifying the long-time decay rate in dispersive estimates for the Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones in terms of the intensity of geometric focusing.
result Each multiplicity of conjugate points within distance π on Y = ∂X0 leads to a |t|1/2-loss in the long-time decay order and a half-order shift in the regularity index in the dispersive estimate for the Schrödinger equation.
Study on stock market volatility and return dispersion during COVID-19.
problem Impact of COVID-19 on stock market volatility and return dispersion.
method Used Google index to proxy epidemic impact, modeled volatility, and analyzed influencing factors of log-return.
result Volatility significantly affected by epidemic and cross-sectional return dispersion, with positive coefficients.
Two methods improve Gaussian process predictive distributions' calibration.
problem Improving the reliability of Gaussian process predictive intervals.
method Introduces two methods: cps-gp and bcr-gp, both adapting conformal predictive systems to GP interpolation.
result Both methods provide finite-sample marginal calibration and smooth predictive distributions.
In the recent years, banks have sold structured products such as worst-of options, Everest and Himalayas, resulting in a short correlation exposure. They have hence become interested in offsetting part of this exposure, namely buying back correlation. Two ways have been proposed for such a strategy : either pure correl…
Private Generative Bootstrap protects privacy in statistical reporting.
problem Protecting privacy in statistical reporting of individual data.
method Bayesian likelihood-free framework with blocking strategy for differential privacy.
result Private Generative Bayesian Bootstrap (PGBB) provides competitive uncertainty quantification.
The study examines Hawkes processes and their long-term behavior.
problem Understanding the long-term behavior of Hawkes processes.
method Proving functional limit theorems under various conditions on the dispersion of child events.
result Functional limit theorems hold for Hawkes processes with different levels of child event dispersion.
Improved BER with reduced power in time-domain digital backpropagation.
problem Improving BER performance in time-domain digital backpropagation.
method Jointly optimized and quantized chromatic dispersion filters using machine learning.
result Improved BER performance and power dissipation reductions.
This paper treats prediction markets as Bayesian inverse problems to quantify uncertainty and identify event outcomes.
problem Uncertainty and identifiability in prediction market outcomes from price-volume histories.
method Formulates prediction markets as Bayesian inverse problems, introduces a log-odds observation model, and derives posterior uncertainty quantification and identifiability criteria.
result Explicit diagnostics for informative and stable inference regimes, and validation through synthetic data experiments.
New framework controls statistical dispersion for high-stakes applications.
problem Understanding and controlling the dispersion of loss distributions in high-stakes applications.
method Simple yet flexible framework for distribution-free control of statistical dispersion measures.
result Proposed methods control statistical dispersion measures with societal implications.
Machine learning improves fiber nonlinearity detection.
problem Mitigating nonlinear effects in optical fiber channels.
method Parzen window classifier applied to detect nonlinear fiber channel.
result Performance improvement in dispersion managed and unmanaged systems.
CUQ-GNN adapts uncertainty quantification for graph data, improving on GPN.
problem Defining meaningful uncertainty on graph data with domain-specific characteristics.
method Combines Graph Neural Networks with Posterior Networks using Normalizing Flows.
result CUQ-GNN produces more flexible and effective uncertainty estimates.
Machine learning classifies surface wave dispersion curves from ambient noise.
problem Classifying surface wave dispersion curves from ambient noise.
method Convolutional neural network (U-net) with transfer learning and supervised learning.
result Machine classification nearly identical to human-picked phases.
Bayesian model tackles spatial count data issues with flexible non-parametric techniques.
problem Challenges in traditional parametric models for spatial count data with unbalanced distributions and complex dependencies.
method Bayesian semi-parametric spatial dispersed count model combining non-parametric techniques and adapted count models.
result Demonstrates superior performance in managing dispersion and capturing intricate spatial patterns.
NBMF improves recommendation precision by modeling count data dispersion.
problem Predicting user preferences in recommender systems with over-dispersed data.
method NBMF extends PF with a multiplicative term to handle over-dispersion, skipping binarization.
result NBMF predicts user tastes more accurately than Poisson matrix factorization.
Decomposes portfolio returns into drift and asset price distribution changes.
problem Understanding efficient markets through portfolio returns and asset price distributions.
method Continuous semimartingale price representations and accounting identity.
result Existence of an asset pricing factor emerges from an accounting identity across various economic and financial environments.
Bayesian Quadrature improves ensembling for neural networks with dispersed likelihood peaks.
problem Ensembling neural networks struggles with dispersed, narrow peaks in likelihood surfaces.
method Uses Bayesian Quadrature to construct weighted ensembles of architectures.
result Empirically outperforms state-of-the-art baselines in test likelihood, accuracy, and expected calibration error.
Study on billiard trajectories with fixed bounces.
problem Counting periodic trajectories with specific bounces.
method Analyzes two-dimensional dispersive billiard systems.
result Asymptotic growth of primitive periodic trajectories.
Classifies Poisson brackets with two variables, finding infinite constants.
problem Classifying dispersive Poisson brackets with two variables.
method Classification through Miura transformations, identifying numerical invariants.
result Infinite constants characterize Miura equivalence classes of brackets.
Paper transforms a complex equation into simpler forms for analysis.
problem Analyzing a fourth-order dispersive flow equation on Kähler manifolds.
method Developed the generalized Hasimoto transformation to simplify the equation.
result Explicit expressions derived for three examples of compact Kähler manifolds.
A new model relaxes constraints on exponential dispersion models.
problem Tight conditions on cumulant function limit the class of exponential dispersion models.
method Introduces K-LED model with Legendre cumulant function and Bregman divergence guidance.
result The model allows for easier computation of mean parameter and includes various distributions.
Physics-informed neural networks simulate solute dispersion in shear flows, validating complex transport mechanisms.
problem Simulating complex solute dispersion in asymmetric reactive environments.
method Physics-informed neural networks (PINNs) embedded with governing equations and boundary conditions.
result PINNs accurately predict solute dispersion, validating transport diagnostics.
Reduced-order model improves LES for atmospheric pollutant dispersion.
problem Accurate near-field pollutant concentration tracking in urban areas.
method Combining POD and GPR for non-intrusive reduced-order modeling.
result Component-by-component optimization captures spatial scales in high-order modes.
Dropout improves regularization in flexible models for rare features.
problem Understanding theoretical properties of dropout in generalized linear models.
method Theoretical analysis and application to adaptive smoothing with B-splines.
result Dropout prefers rare features in mean and dispersion parameters.
The standard deviation and Gini mean difference order based on tail behavior.
problem Ordering between standard deviation and Gini mean difference for real-valued risks.
method Analysis of the mean excess function of the pairwise difference ∣X−X′∣. result Dominance regimes of SD and GMD are determined by tail behavior of the distribution.
Study dispersive estimates for Schrödinger and wave equations on a cone with specific metric.
problem Pointwise decay estimates for Schrödinger and wave equations on a product cone.
method Modified Hadamard parametrix on Y with ε>π to prove dispersive estimates. result Threshold of conjugate radius ε>π for pointwise dispersive estimates. Proposes a new portfolio optimization method considering reward, dispersion, and asymmetry.
problem Capturing fat-tails and asymmetry in asset return distributions.
method Market model with tempered stable distribution; extended mean-variance optimization.
result Closed-form solutions for VaR and CVaR; efficient frontier extended to three dimensions.
We discuss a short-time existence theorem of solutions to the initial value problem for a third order dispersive flow for closed curves into a compact almost Hermitian manifold. Our equations geometrically generalize a physical model describing the motion of vortex filament. The classical energy method cannot work for …
The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determination of s…
Neural networks improve wave-equation simulation accuracy.
problem Inaccurate discretization of Laplacian in wave-equation simulation leads to numerical dispersion.
method Intersperse CNNs between low-fidelity timesteps to correct wavefield and limit numerical dispersion.
result Neural network augmentation reduces numerical dispersion artifacts in wave-equation simulation.
Method meta-classifies semantic segmentation predictions using dispersion measures.
problem Assessing the quality of semantic segmentation predictions.
method Aggregates dispersion measures (entropy) of predicted probabilities to derive metrics correlated with IoU.
result Metrics correlate well with IoU, providing reliable prediction quality ratings.
The purpose of this paper is to study the generalized Fong--Vasicek two-factor interest rate model with stochastic volatility. In this model the dispersion of the stochastic short rate (square of volatility) is assumed to be stochastic as well and it follows a non-negative process with volatility proportional to the sq…
The paper examines stochastic ordering of Gini indexes for multivariate elliptical risks.
problem Stochastic ordering of Gini indexes for multivariate elliptical risks.
method Established conditions for monotonicity of Gini index in usual stochastic order.
result Suitable conditions for multivariate elliptical risks generalize those for multivariate normal risks.
We study productivity dispersions across workers, firms and industrial sectors. Empirical study of the Japanese data shows that they all obey the Pareto law, and also that the Pareto index decreases with the level of aggregation. In order to explain these two stylized facts, we propose a theoretical framework built upo…
This paper models default data to capture dynamic dependence across sectors.
problem Static models fail to explain monthly default dependence.
method Dynamic low-rank state-space model for monthly multi-sector default-count data.
result Effective correlation matrices and copulas are induced from monthly data.
Improved peak detection in ChIP-seq data reduces over-dispersion.
problem Over-dispersion in ChIP-seq data reduces peak detection accuracy.
method Supervised segmentation models with alternative noise assumptions.
result Improved peak detection accuracy compared to natural assumptions.
New model improves DNA methylation data analysis.
problem Analyzing DNA methylation data with complex distributions.
method Doubly non-central beta (DNCB) distribution for non-negative matrix factorization.
result Improves predictive performance and yields meaningful latent representations.
Temporal coarse-graining of multi-sector default count data generates effective correlation matrices and rank copulas.
problem Explaining the difference in default dependence between monthly and annual aggregation.
method Dynamic low-rank state-space model with AR(1) latent credit-state factors.
result Effective correlation matrices and rank copulas are generated from monthly default count data.