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.
The study examines Fisher-Riemann geodesics for nonparametric probability densities.
Discussing new econophysics methods for volatility and probability density estimation.
Paper connects probability density cuts to graph theory eigenfunctions.
We show a general relation between the spatially disjoint product of probability density functions and the sum of their Fisher information metric tensors. We then utilise this result to give a method for constructing the probability density functions for an arbitrary Riemannian Fisher information metric tensor. We note…
VI approximates complex densities faster than classical methods.
The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units. We derive that the corresponding conditional density function is given by a reparameterization of the Riemann-Theta Boltzmann machine modelling the original probability…
We present a novel synthesis of Fisher information and asset pricing theory that yields a practical method for reconstructing the probability density implicit in security prices. The Fisher information approach to these inverse problems transforms the search for a probability density into the solution of a differential…
DoSE improves OOD detection by estimating model probability density.
Transformer with denoising diffusion improves probabilistic density estimation.
Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions. More recent generative models leverage the power of neural networks to implicitly learn and represent probability …
Method estimates joint probability density from samples using low-rank decomposition and random projections.
Quantum probability theory reveals hidden structure in joint probability distributions.
Proposes a method to estimate time-dependent probability density functions using binary classifiers.
This paper introduces PM and PMLP to enhance SSL by considering probability density and cluster assumptions.
This paper introduces a probability density estimator based on Green's function identities. A density model is constructed under the sole assumption that the probability density is differentiable. The method is implemented as a binary likelihood estimator for classification purposes, so issues such as mis-modeling and …
Chia and Nakano (2009) introduced the concept of M-decomposability of probability densities in one-dimension. In this paper, we generalize M-decomposability to any dimension. We prove that all elliptical unimodal densities are M-undecomposable. We also derive an inequality to show that it is better to represent an M-de…
A new machine learning model uses score matching to estimate probability densities efficiently.
New tractable density models from squaring neural networks.
Data analysis in high-dimensional spaces aims at obtaining a synthetic description of a data set, revealing its main structure and its salient features. We here introduce an approach providing this description in the form of a topography of the data, namely a human-readable chart of the probability density from which t…
A new density model using Fourier basis achieves better approximations and compression.
Hierarchical nucleation patterns emerge in deep neural network layers.
Bayesian inference engines improve density estimation accuracy and scalability.
We introduce a novel conditional density estimation model termed the conditional density operator (CDO). It naturally captures multivariate, multimodal output densities and shows performance that is competitive with recent neural conditional density models and Gaussian processes. The proposed model is based on a novel …
Estimates copula density for complex data distributions.
Instantaneous volatility of logarithmic return in the lognormal fractional SABR model is driven by the exponentiation of a correlated fractional Brownian motion. Due to the mixed nature of driving Brownian and fractional Brownian motions, probability density for such a model is less studied in the literature. We show i…
PSD models simplify probability density estimation.
An image pattern can be represented by a probability distribution whose density is concentrated on different low-dimensional subspaces in the high-dimensional image space. Such probability densities have an astronomical number of local modes corresponding to typical pattern appearances. Related groups of modes can join…
A new algorithm for sampling from complex distributions.
New method approximates high-dimensional probability densities efficiently.
This research improves demand forecasting by predicting complete probability density functions using machine learning.
A density ratio is defined by the ratio of two probability densities. We study the inference problem of density ratios and apply a semi-parametric density-ratio estimator to the two-sample homogeneity test. In the proposed test procedure, the f-divergence between two probability densities is estimated using a density-r…
This work introduces a novel nonparametric density index defined on graphs, the Sum-over-Forests (SoF) density index. It is based on a clear and intuitive idea: high-density regions in a graph are characterized by the fact that they contain a large amount of low-cost trees with high outdegrees while low-density regions…
We show that the visible sector probability density function of the Riemann-Theta Boltzmann machine corresponds to a gaussian mixture model consisting of an infinite number of component multi-variate gaussians. The weights of the mixture are given by a discrete multi-variate gaussian over the hidden state space. This a…
New method improves sampling from high-dimensional target densities.
We introduce a new random group model called the square model: we quotient a free group on generators by a random set of relations, each of which is a reduced word of length four. We prove, as in the Gromov density model, that for densities a random group in the square model is trivial with overwhel…
Conformal Prediction Regions match Imprecise Highest Density Regions under consonance.
The geometric approach to optimal transport and information theory has triggered the interpretation of probability densities as an infinite-dimensional Riemannian manifold. The most studied Riemannian structures are Otto's metric, yielding the -Wasserstein distance of optimal mass transport, and the Fisher--Rao me…
Regularized mixtures improve inflation and interest rate forecasts, especially correcting overconfidence.
AdaAnn optimizes annealing for efficient probability density approximation.
By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty. The uncertainty information can be particularly meaningful in capturing entailment relationships -- whereby general words such as "entity" co…
OPAA estimates probability densities using functional analysis.
Paper proposes new density estimators for high-dimensional data.
Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classificati…
Deep belief networks can approximate any multivariate density with binary hidden units.
Paper develops polynomial approximations for complex probability densities.
We study the rank distribution, the cumulative probability, and the probability density of returns of stock prices of listed firms traded in four stock markets. We find that the rank distribution and the cumulative probability of stock prices traded in are consistent approximately with the Zipf's law or a power law. It…
Kernel Density Machines learn probability densities without structural assumptions.
We apply the formalism of the continuous time random walk to the study of financial data. The entire distribution of prices can be obtained once two auxiliary densities are known. These are the probability densities for the pausing time between successive jumps and the corresponding probability density for the magnitud…