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 paper updates Bayesian CMA-ES with normal Wishart and proves lower expected covariance.
A method for converting NIW parameters for better estimation.
We show that the only parameter prior for complete Gaussian DAG models that satisfies global parameter independence, complete model equivalence, and some weak regularity assumptions, is the normal-Wishart distribution. Our analysis is based on the following new characterization of the Wishart distribution: let W be an …
Develops methods for constructing parameter priors in DAG models.
Paper explores Elliptical Wishart distributions in signal processing and machine learning.
New method uses KL-divergence to create non-informative priors for multivariate Gaussian.
Bayesian approach improves CMA-ES algorithm for faster convergence.
We consider a short rate model, driven by a stochastic process on the cone of positive semidefinite matrices. We derive sufficient conditions ensuring that the model replicates normal, inverse or humped yield curves.
Develops a new MCMC-based Wishart prior for Gaussian Process covariance matrix.
Regression Prior Networks improve ensemble performance on regression tasks.
The paper develops scalable Bayesian models for dynamic covariance matrices using Gaussian processes.
Bayesian framework for analyzing heterogeneous covariance data with a novel MoE-Wishart model.
Study proposes a new model for joint survival annuity valuation.
New framework for calculating multivariate risk measures using Wishart process.
This work deals with the simulation of Wishart processes and affine diffusions on positive semidefinite matrices. To do so, we focus on the splitting of the infinitesimal generator, in order to use composition techniques as Ninomiya and Victoir or Alfonsi. Doing so, we have found a remarkable splitting for Wishart proc…
Gaussian graphical models are relevant tools to learn conditional independence structure between variables. In this class of models, Bayesian structure learning is often done by search algorithms over the graph space. The conjugate prior for the precision matrix satisfying graphical constraints is the well-known G-Wish…
Improved variational approximation for deep Wishart process models.
WISDoM uses the Wishart distribution to analyze neurological data like EEG and brain connectivity.
The paper studies the distribution of random degeneracy sets on complex manifolds.
Bayesian inference for stochastic differential equations using Wishart diffusions.
Researchers develop a new SMC sampler for Wishart processes to improve dynamic covariance inference.
Researchers derive an explicit Laplace transform for integrated Volterra Wishart process.
A new method for deep Wishart processes improves kernel-based models.
New distribution simplifies covariance matrix inference.
A non-Hermitean extension of paradigmatic Wishart random matrices is introduced to set up a theoretical framework for statistical analysis of (real, complex and real quaternion) stochastic time series representing two "remote" complex systems. The first paper in a series provides a detailed spectral theory of non-Hermi…
Lower bounds show linear complexity for linear regression.
Deep kernel processes unify various models using Gram matrices and kernel functions.
The scaled complex Wishart distribution is a widely used model for multilook full polarimetric SAR data whose adequacy has been attested in the literature. Classification, segmentation, and image analysis techniques which depend on this model have been devised, and many of them employ some type of dissimilarity measure…
Cone structures in quantum field theory linked to information geometry.
We derive the explicit formula for the joint Laplace transform of the Wishart process and its time integral which extends the original approach of Bru. We compare our methodology with the alternative results given by the variation of constants method, the linearization of the Matrix Riccati ODE's and the Runge-Kutta al…
We propose a new input perturbation mechanism for publishing a covariance matrix to achieve -differential privacy. Our mechanism uses a Wishart distribution to generate matrix noise. In particular, We apply this mechanism to principal component analysis. Our mechanism is able to keep the positive semi-definitene…
We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP). It is a collection of positive semi-definite random matrices indexed by any arbitrary dependent variable. We use it to model dynamic (e.g. time varying) covariance matrices. Unlike existing models, it can capture a diverse …
A Bayesian procedure is developed for multivariate stochastic volatility, using state space models. An autoregressive model for the log-returns is employed. We generalize the inverted Wishart distribution to allow for different correlation structure between the observation and state innovation vectors and we extend the…
Iterative method 'Concent' corrects spectrum bias in covariance matrices.
The traditional Minkowski distances are induced by the corresponding Minkowski norms in real-valued vector spaces. In this work, we propose novel statistical symmetric distances based on the Minkowski's inequality for probability densities belonging to Lebesgue spaces. These statistical Minkowski distances admit closed…
Study on Gaussian ensemble of matrix products with mixed moments computed.
BayPrAnoMeta tackles few-shot industrial image anomaly detection with Bayesian methods.
In this article, we propose an exact simulation method of the Wishart multidimensional stochastic volatility (WMSV) model, which was recently introduced by Da Fonseca et al. \cite{DGT08}. Our method is based onanalysis of the conditional characteristic function of the log-price given volatility level. In particular, we…
We are concerned with an approximation problem for a symmetric positive semidefinite matrix due to motivation from a class of nonlinear machine learning methods. We discuss an approximation approach that we call {matrix ridge approximation}. In particular, we define the matrix ridge approximation as an incomplete matri…
A new multivariate stochastic volatility estimation procedure for financial time series is proposed. A Wishart autoregressive process is considered for the volatility precision covariance matrix, for the estimation of which a two step procedure is adopted. The first step is the conditional inference on the autoregressi…
This thesis consists of two independent parts: random matrices, which form the first one-third of this thesis, and machine learning, which constitutes the remaining part. The main results of this thesis are as follows: a necessary and sufficient condition for the inverse moments of -Laguerre matrices and compo…
We prove a large deviations principle for the class of multidimensional affine stochastic volatility models considered in (Gourieroux, C. and Sufana, R., J. Bus. Econ. Stat., 28(3), 2010), where the volatility matrix is modelled by a Wishart process. This class extends the very popular Heston model to the multivariate …
Study on eigenvalue distribution of correlated time series, showing deformation of Marchenko-Pastur distribution.
In this paper, we obtain a property of the expectation of the inverse of compound Wishart matrices which results from their orthogonal invariance. Using this property as well as results from random matrix theory (RMT), we derive the asymptotic effect of the noise induced by estimating the covariance matrix on computing…
LoRA and privacy: Random projections help but not always.
Bayesian semi-supervised learning for multi-class classification.
We put forward a complete theory on moment explosion for fairly general state-spaces. This includes a characterization of the validity of the affine transform formula in terms of minimal solutions of a system of generalized Riccati differential equations. Also, we characterize the class of positive semidefinite process…
New method allows generating independent data matrices from summary statistics.