arXiv research

Paper develops efficient method for probability estimation.

The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions. The cluster structure allows the probability distribution of the random partitions of a subset of the sample to be dependent on the sample size, a feature not presented in a …

Estimates inverse temperature of Ising models with a single sample.

A common approach to analyze a covariate-sample count matrix, an element of which represents how many times a covariate appears in a sample, is to factorize it under the Poisson likelihood. We show its limitation in capturing the tendency for a covariate present in a sample to both repeat itself and excite related ones…

The paper models stock returns using $q$-Gaussians and negative binomials.

Soft Truncation improves diffusion model performance by balancing loss scales across diffusion times.

New sampling method for Heston model reduces complexity.

IBS efficiently estimates log-likelihood without bias.

We consider large scale empirical risk minimization (ERM) problems, where both the problem dimension and variable size is large. In these cases, most second order methods are infeasible due to the high cost in both computing the Hessian over all samples and computing its inverse in high dimensions. In this paper, we pr…

The study tightens bounds on binomial probabilities and minimums using KL-divergence.

We introduce a new class of forward performance processes that are endogenous and predictable with regards to an underlying market information set and, furthermore, are updated at discrete times. We analyze in detail a binomial model whose parameters are random and updated dynamically as the market evolves. We show tha…

Efficient Bayesian variable selection for binomial and negative binomial data.

Develops a data augmentation method for models with gamma functions.

The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix. As the marginal probability distribution of the BNBP that governs the exchangeable random partitions of grouped data has not yet been developed, current inference f…

Established PFPPs in complete markets, solving integral equations.

Efficiently estimate Boolean product distribution parameters from truncated samples.

Study certifies missed relevant items in candidate generation with audit labels.

Paper develops IFTRR to solve sparse generalized eigenvalue problems efficiently.

Improved concentration inequalities for sub-Weibull variables enhance statistical and machine learning applications.

Optimal algorithm learns Gaussian under halfspace truncation with minimal samples.

A new method reduces the bias in estimating inverse covariance matrices from sketches.

PMT uses public data moments to make DP feasible for unbounded data.

Paper proposes a new estimator for generic discrete distributions.

Two approaches improve parameter learning in various mixture models.

Algorithm estimates Gaussian parameters under unknown truncation sets.

The paper investigates the convergence of Vendi scores under finite samples and introduces a truncated version for better performance.

This paper considers binomial approximation of continuous time stochastic processes. It is shown that, under some mild integrability conditions, a process can be approximated in mean square sense and in other strong metrics by binomial processes, i.e., by processes with fixed size binary increments at sampling points. …

We study a novel spline-like basis, which we name the "falling factorial basis", bearing many similarities to the classic truncated power basis. The advantage of the falling factorial basis is that it enables rapid, linear-time computations in basis matrix multiplication and basis matrix inversion. The falling factoria…

Paper uses algebraic signatures to identify probabilistic structures in empirical data.

Adaptive Nucleus Truncation Improves Long-Form Reasoning

A new algorithm speeds up elliptical slice sampling for truncated multivariate normals.

Unified framework for mean testing under truncation bias.

Estimates set overlap and similarity using random samples.

The paper efficiently estimates parameters from truncated Gaussian and linear models.

A new control chart detects shifts in binary data streams quickly and reliably.

New JPEG algorithm corrects biases in endogenously truncated data.

Detecting and recovering labels in binomial logistic mixtures is challenging due to an information gap.

Extends diffusion models to handle exponential family distributions for inverse problems.

Truncated CauchyNMF robustly learns subspaces from noisy data.

BKP R package models spatially varying binomial probabilities efficiently.

We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a $d$-variate normal ${\cal N}(\mathbfμ,\mathbfΣ)$ means a samples is only re…

The $k$th-nearest neighbor rule is arguably the simplest and most intuitively appealing nonparametric classification procedure. However, application of this method is inhibited by lack of knowledge about its properties, in particular, about the manner in which it is influenced by the value of $k$; and by the absence of…

We construct a binomial tree model fitting all moments to the approximated geometric Brownian motion. Our construction generalizes the classical Cox-Ross-Rubinstein, the Jarrow-Rudd, and the Tian binomial tree models. The new binomial model is used to resolve a discontinuity problem in option pricing.

By developing data augmentation methods unique to the negative binomial (NB) distribution, we unite seemingly disjoint count and mixture models under the NB process framework. We develop fundamental properties of the models and derive efficient Gibbs sampling inference. We show that the gamma-NB process can be reduced …

Bayesian method estimates LTLL distribution parameters for time-to-event data.

Lower bound shows super-polynomial gap for estimating truncated Gaussian means.

Extends CRR model with q-binomial random walks for asset pricing.

Paper proposes approximate Stein classes for efficient truncated density estimation.