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.
Modified Metropolis algorithm ensures convergence for multivariate binary distributions with fixed-order updates.
New MCMC method corrects bias without extra cost.
We introduce an adaptive output-sensitive Metropolis-Hastings algorithm for probabilistic models expressed as programs, Adaptive Lightweight Metropolis-Hastings (AdLMH). The algorithm extends Lightweight Metropolis-Hastings (LMH) by adjusting the probabilities of proposing random variables for modification to improve c…
SMTM improves MCMC sampling in high dimensions with multiple proposals and stereographic integration.
Develops algorithm to differentiate Metropolis-Hastings for optimization.
cKAM improves adaptive sampling by incorporating a cyclical stepsize scheme.
A new Metropolis-Hastings algorithm uses Gaussian Processes to speed up sampling from complex models.
Study optimizes step size for Metropolis algorithm in non-identifiable cases.
New algorithm improves sampling from constrained spaces.
Optimizes Metropolis-Hastings algorithms for efficient sampling in high dimensions.
Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems. The Markov Chain Monte Carlo (MCMC) algorithms are a well-known class of MC methods which generate a Markov chain with the desired invariant distribution. In this document, we focus on the Metropolis…
New tuning rules for Metropolis algorithms derived from Bayesian large-sample asymptotics.
Recent works propose using the discriminator of a GAN to filter out unrealistic samples of the generator. We generalize these ideas by introducing the implicit Metropolis-Hastings algorithm. For any implicit probabilistic model and a target distribution represented by a set of samples, implicit Metropolis-Hastings oper…
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computation of {\it a-posteriori} estimators which are often expressed as complicated multi-dimensional integrals. Unfortunately, analytical expressi…
New sampling method improves accuracy for constrained spaces.
MAFLA improves sampling from heavy-tailed distributions using MH-inspired corrections.
Combines MALA and Adam for efficient uncertainty quantification in deep learning.
A new two-step MH method for Bayesian EL computation.
Improved spectral gap for MwG with adaptive RWM proposals.
The exchange algorithm is studied for its convergence and asymptotic variance.
We apply the hybrid Monte Carlo (HMC) algorithm to the financial time sires analysis of the stochastic volatility (SV) model for the first time. The HMC algorithm is used for the Markov chain Monte Carlo (MCMC) update of volatility variables of the SV model in the Bayesian inference. We compute parameters of the SV mod…
A DP method selects best sparse models in high dimensions efficiently.
The study examines convergence of stochastic processes on large graphs and adjacency matrices.
Algorithm samples constrained stochastic differential equations.
A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the purpose of sampling from a target distribution with strongly nonlinear support. The algorithm embeds the trajectory of the Markov chain into a reproducing kernel Hilbert space (RKHS), such that the feature space covariance of the samples informs the…
New bounds for MCMC on discrete spaces without dimension dependence.
Bayesian inference via standard Markov Chain Monte Carlo (MCMC) methods is too computationally intensive to handle large datasets, since the cost per step usually scales like in the number of data points . We propose the Scalable Metropolis-Hastings (SMH) kernel that exploits Gaussian concentration of the pos…
We consider the problem of sampling from a strongly log-concave density in , and prove a non-asymptotic upper bound on the mixing time of the Metropolis-adjusted Langevin algorithm (MALA). The method draws samples by simulating a Markov chain obtained from the discretization of an appropriate Langevin dif…
Paper presents a fast, private MH algorithm for large-scale Bayesian inference.
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian inference in models where the posterior distribution is analytical intractable or computationally costly to evaluate directly. It operates by introducing additional auxiliary variables into the model and form an extended target distribution, w…
Particle MCMC is a class of algorithms that can be used to analyse state-space models. They use MCMC moves to update the parameters of the models, and particle filters to propose values for the path of the state-space model. Currently the default is to use random walk Metropolis to update the parameter values. We show …
The hybrid Monte Carlo (HMC) algorithm is applied for the Bayesian inference of the stochastic volatility (SV) model. We use the HMC algorithm for the Markov chain Monte Carlo updates of volatility variables of the SV model. First we compute parameters of the SV model by using the artificial financial data and compare …
This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm …
We introduce a new geometric approach that constructs a transition kernel of Markov chain. Our method always minimizes the average rejection rate and even reduce it to zero in many relevant cases, which cannot be achieved by conventional methods, such as the Metropolis-Hastings algorithm or the heat bath algorithm (Gib…
New method samples Jeffreys prior for objective Bayesian inference.
We perform Markov chain Monte Carlo simulations for a Bayesian inference of the GJR-GARCH model which is one of asymmetric GARCH models. The adaptive construction scheme is used for the construction of the proposal density in the Metropolis-Hastings algorithm and the parameters of the proposal density are determined ad…
New method improves sampling from score-based models by correcting bias.
Oracle inequality for sparse neural nets adapts to unknown structure.
Markov Chain Monte Carlo (MCMC) methods have a drawback when working with a target distribution or likelihood function that is computationally expensive to evaluate, specially when working with big data. This paper focuses on Metropolis-Hastings (MH) algorithm for unimodal distributions. Here, an enhanced MH algorithm …
Study on Metropolis-within-Gibbs schemes for high-dimensional Bayesian models.
A new algorithm speeds up rerandomization for better experiment balance.
Optimal preconditioning improves Langevin sampling efficiency.
Improved MTM algorithm reduces high-dimensional convergence issues.
We propose a new class of learning algorithms that combines variational approximation and Markov chain Monte Carlo (MCMC) simulation. Naive algorithms that use the variational approximation as proposal distribution can perform poorly because this approximation tends to underestimate the true variance and other features…
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In th…
Bayesian inference for expensive likelihoods using Langevin Monte Carlo with NF.
Exact minibatch MH method improves scalability for large datasets.
New sampling methods improve statistical efficiency for intractable targets.