A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Develops a new framework for estimating joint probability distributions.
Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation to measure the discrepancy between the source and target domain distributions. Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the condi…
Novel approach for estimating joint probability densities using tensor decompositions and dictionaries.
Estimates joint probability distribution from 1-way marginals using low-rank tensors and random projections.
Method estimates joint probability density from samples using low-rank decomposition and random projections.
Proposes IPT for modeling complex joint distributions.
FJS method improves multinomial classification accuracy.
Proposes a new model to better handle correlation risk in credit risk calculations.
Quantum probability theory reveals hidden structure in joint probability distributions.
SJS model predicts label shifts in multinomial datasets.
We provide a distribution-free test that can be used to determine whether any two joint distributions and are statistically different by inspection of a large enough set of samples. Following recent efforts from Long et al. [1], we rely on joint kernel distribution embedding to extend the kernel two-sample test…
We present a novel approach for estimating conditional probability tables, based on a joint, rather than independent, estimate of the conditional distributions belonging to the same table. We derive exact analytical expressions for the estimators and we analyse their properties both analytically and via simulation. We …
The paper analyzes multivariate Hawkes processes and their induced population processes.
The most direct way to express arbitrary dependencies in datasets is to estimate the joint distribution and to apply afterwards the argmax-function to obtain the mode of the corresponding conditional distribution. This method is in practice difficult, because it requires a global optimization of a complicated function,…
An important application of Lebesgue integral quadrature arXiv:1807.06007 is developed. Given two random processes, and , two generalized eigenvalue problems can be formulated and solved. In addition to obtaining two Lebesgue quadratures (for and ) from two eigenproblems, the projections of - and…
A central tenet of probabilistic programming is that a model is specified exactly once in a canonical representation which is usable by inference algorithms. We describe JointDistributions, a family of declarative representations of directed graphical models in TensorFlow Probability.
Proposes a new model for joint probability distributions in computer vision.
The problem of determining the joint probability distributions for correlated random variables with pre-specified marginals is considered. When the joint distribution satisfying all the required conditions is not unique, the "most unbiased" choice corresponds to the distribution of maximum entropy. The calculation of t…
There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder's parameters to respect autoregressive constraints: ea…
The ability to estimate joint, conditional and marginal probability distributions over some set of variables is of great utility for many common machine learning tasks. However, estimating these distributions can be challenging, particularly in the case of data containing a mix of discrete and continuous variables. Thi…
GFlowNets sample diverse candidates in active learning.
Generative model for joint discrete distributions using randomized assignment flows.
Study improves probabilistic circuits using transformations for better predictions.
The article explains the probabilistic method of default probability estimation by Pluto and Tasche.
Estimating the joint probability mass function (PMF) of a set of random variables lies at the heart of statistical learning and signal processing. Without structural assumptions, such as modeling the variables as a Markov chain, tree, or other graphical model, joint PMF estimation is often considered mission impossible…
New scheme optimizes BMI through probabilistic and geometric shaping.
Econometric framework integrates heavy-tailed distributions with behavioral probability weighting for better asset pricing.
Generative models for graphs have been typically committed to strong prior assumptions concerning the form of the modeled distributions. Moreover, the vast majority of currently available models are either only suitable for characterizing some particular network properties (such as degree distribution or clustering coe…
We characterize the set of market models when there are a finite number of traded Vanilla and Barrier options with maturity written on the asset . From a probabilistic perspective, our result describes the set of joint distributions for when a finite number of marginal law constraint…
The paper bounds and identifies joint probabilities in causal inference with monotonicity assumptions.
There has recently been considerable interest in completing a low-rank matrix or tensor given only a small fraction (or few linear combinations) of its entries. Related approaches have found considerable success in the area of recommender systems, under machine learning. From a statistical estimation point of view, the…
MPSTime uses matrix-product states for efficient time-series ML.
Paper studies matching of samples from two distributions with a Gibbs probability weight.
This work extends stochastic localization to joint probability measures for data analysis.
This work proposes a new method to estimate joint probability from pairwise marginals, reducing sample complexity.
This paper presents a Bayesian method for estimating the rank of a low-rank tensor model of joint PMF.
A market-maker-based prediction market lets forecasters aggregate information by editing a consensus probability distribution either directly or by trading securities that pay off contingent on an event of interest. Combinatorial prediction markets allow trading on any event that can be specified as a combination of a …
Paper introduces CWDAE for better synthetic data generation.
There are many advantages to use probability method for nonlinear system identification, such as the noises and outliers in the data set do not affect the probability models significantly; the input features can be extracted in probability forms. The biggest obstacle of the probability model is the probability distribu…
Classifier chains are popular and effective method to tackle a multi-label classification problem. The aim of this paper is to study the asymptotic properties of the chain model in which the conditional probabilities are of the logistic form. In particular we find conditions on the number of labels and the distribution…
AR-CSM models use derivatives of univariate log-conditionals to estimate joint distributions efficiently.
A new method infers graph structure and parameters using a single generative flow network.
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most signifi…
Introduces joint exclusivity (JE), a new form of negative dependence.
A new multivariate distribution possessing arbitrarily parametrized and positively dependent univariate Pareto margins is introduced. Unlike the probability law of Asimit et al. (2010) [Asimit, V., Furman, E. and Vernic, R. (2010) On a multivariate Pareto distribution. Insurance: Mathematics and Economics 46(2), 308-31…
Sharp error bounds derived for bidirectional GANs without restrictive assumptions.
New tree-structured Markov fields with Poisson marginals for counting variables.
Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty…