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.
Improved MALA method for neural networks uncertainty quantification.
Bayesian inference for Levy density with Gibbs posterior in discrete sampling.
A new method improves uncertainty quantification in Bayesian inference.
The paper proposes a Gibbs sampler for neural network posterior sampling.
In the popular approach of "Bayesian variable selection" (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with a Gibbs posterior originating in statistical mechanics. The Gibbs posterior is con…
New methods for tuning alpha in Gibbs posteriors improve speed and accuracy.
PAC-Bayes bounds for Gibbs posteriors derived via singular learning theory.
Develops a Bayesian framework for portfolio choice with a new posterior distribution.
The PAC-Bayesian approach is a powerful set of techniques to derive non- asymptotic risk bounds for random estimators. The corresponding optimal distribution of estimators, usually called the Gibbs posterior, is unfortunately intractable. One may sample from it using Markov chain Monte Carlo, but this is often too slow…
New diagnostic tool for assessing approximate Bayesian inference.
New algorithm improves mixing in Bayesian mixture models.
New MCMC methods improve efficiency for large network inference.
The paper extends entropy maximization to multiscale settings and applies it to neural networks.
Paper introduces a Gibbs sampler for Bayesian inversion of ill-posed problems.
Researchers estimate optimal PAC-Bayes bounds using Hamiltonian Monte Carlo.
Combines MALA and Adam for efficient uncertainty quantification in deep learning.
We develop a framework for approximating collapsed Gibbs sampling in generative latent variable cluster models. Collapsed Gibbs is a popular MCMC method, which integrates out variables in the posterior to improve mixing. Unfortunately for many complex models, integrating out these variables is either analytically or co…
GDiff tackles blind denoising with Gibbs sampling and Monte Carlo inference.
New methods improve sampling from complex dynamical models.
This work analyzes Gibbs samplers for Bayesian hierarchical models without dimensionality constraints.
The Gibbs algorithm's generalization error is bounded, improving with prior volume in low temperatures.
We investigate a class of feature allocation models that generalize the Indian buffet process and are parameterized by Gibbs-type random measures. Two existing classes are contained as special cases: the original two-parameter Indian buffet process, corresponding to the Dirichlet process, and the stable (or three-param…
This paper addresses the mapping problem. Using a conjugate prior form, we derive the exact theoretical batch multi-object posterior density of the map given a set of measurements. The landmarks in the map are modeled as extended objects, and the measurements are described as a Poisson process, conditioned on the map. …
In this work, we propose a model for estimating volatility from financial time series, extending the non-Gaussian family of space-state models with exact marginal likelihood proposed by Gamerman, Santos and Franco (2013). On the literature there are models focused on estimating financial assets risk, however, most of t…
A new framework for clustering with uncertainty quantification.
A fundamental task in machine learning and related fields is to perform inference on Bayesian networks. Since exact inference takes exponential time in general, a variety of approximate methods are used. Gibbs sampling is one of the most accurate approaches and provides unbiased samples from the posterior but it has hi…
New algorithms improve Bayesian linear regression with spike-and-slab priors.
Improved Bayesian regression for large datasets using multilevel Gibbs sampling.
GADD accelerates uniform-rate discrete diffusion models by 2 orders of magnitude.
New Gibbs sampling method improves MCMC efficiency.
Bayesian feature allocation models are a popular tool for modelling data with a combinatorial latent structure. Exact inference in these models is generally intractable and so practitioners typically apply Markov Chain Monte Carlo (MCMC) methods for posterior inference. The most widely used MCMC strategies rely on an e…
The paper tackles sampling from Gibbs measures with constrained support, providing a sampling guarantee.
New method reduces computational cost for estimating PAC-Bayes bounds.
This paper develops a matrix-variate adaptive Markov chain Monte Carlo (MCMC) methodology for Bayesian Cointegrated Vector Auto Regressions (CVAR). We replace the popular approach to sampling Bayesian CVAR models, involving griddy Gibbs, with an automated efficient alternative, based on the Adaptive Metropolis algorith…
The focus in this paper is Bayesian system identification based on noisy incomplete modal data where we can impose spatially-sparse stiffness changes when updating a structural model. To this end, based on a similar hierarchical sparse Bayesian learning model from our previous work, we propose two Gibbs sampling algori…
New algorithm improves efficiency of Bayesian Causal Forest for subgroup analysis.
Monte Carlo methods are essential tools for Bayesian inference. Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning, and statistics, employed to draw samples from complicated high-dimensional posterior distributions. The key point for the suc…
Improved Gibbs sampler speeds up Bayesian exponential smoothing model.
The SLOPE estimates regression coefficients by minimizing a regularized residual sum of squares using a sorted--norm penalty. The SLOPE combines testing and estimation in regression problems. It exhibits suitable variable selection and prediction properties, as well as minimax optimality. This paper introduces …
In this study, we present a multi-class graphical Bayesian predictive classifier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. For each class, the dependence structure underlying the observed features is represented by a set of decomposable Gaussian graphical models. Emp…
The standard Gibbs sampler of Mixed Multinomial Logit (MMNL) models involves sampling from conditional densities of utility parameters using Metropolis-Hastings (MH) algorithm due to unavailability of conjugate prior for logit kernel. To address this non-conjugacy concern, we propose the application of Pólygamma data a…
We develop a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a gamma process. We derive a posterior characterization and a simple and effective Gibbs…
We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models. The approach is based on mapping distributed, symmetry-invariant representations of cluster arrangements into conditional probabilities. Th…
We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frames structured variational inference as adaptive importance sampling. APG samplers construct high-dimensional proposals by iterating over updates to lower-dimensional blocks of variables. We train each conditional proposal by mini…
Proposes a new SPVM model for RVM with more flexible priors.
Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic models has successfully integrated it with Bayesian topic models to discover discriminative latent semantic structures and make accurate predictions for unseen testing data. Ho…
New decision-theoretic characterization separates belief and decision posteriors.
Paper introduces a new sampling method for Bayesian inference.