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.
Enhanced Markov chain sampler learns network statistics faster.
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improv…
PL-MCMC samples from normalizing flows' conditional distributions.
A new eigenvalue-based method speeds up Monte Carlo simulations.
TensorFlow Probability MCMC toolkit improves MCMC efficiency for modern hardware.
Combines normalizing flows and quasi-Monte Carlo for improved numerical integration.
The paper improves SMC algorithm for multi-modal distributions by proving variance bounds.
Unified framework for MCMC and machine learning problems.
Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that in general performing exact inference requires all of the data to be processed at eac…
Paper analyzes and accelerates Langevin Monte Carlo methods using large deviations theory.
New algorithms improve MCMC efficiency for complex distributions.
Estimates covariance matrices using Markov chain Monte Carlo with improved sample complexity.
We explore a general framework in Markov chain Monte Carlo (MCMC) sampling where sequential proposals are tried as a candidate for the next state of the Markov chain. This sequential-proposal framework can be applied to various existing MCMC methods, including Metropolis-Hastings algorithms using random proposals and m…
ParaMonte::Python streamlines Bayesian data analysis with fast Monte Carlo and MCMC routines.
Improved sampling for network community detection.
New algorithms improve sampling from complex distributions.
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…
Improved Bayesian regression for large datasets using multilevel Gibbs sampling.
Generative models map simple samples to complex target samples.
A new method combines AIS and SMCI for efficient evaluation of Ising models.
The paper provides privacy guarantees for MCMC algorithms using Langevin dynamics.
We propose a novel framework of estimating systemic risk measures and risk allocations based on Markov chain Monte Carlo (MCMC) methods. We consider a class of allocations whose jth component can be written as some risk measure of the jth conditional marginal loss distribution given the so-called crisis event. By consi…
We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space. We here focus on modelling nonlinear covariance s…
This paper analyzes the bias of inexact MCMC methods in high dimensions.
We describe parallel Markov chain Monte Carlo methods that propagate a collective ensemble of paths, with local covariance information calculated from neighboring replicas. The use of collective dynamics eliminates multiplicative noise and stabilizes the dynamics thus providing a practical approach to difficult anisotr…
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov chain Monte Carlo me…
New algorithm MTMC reduces MCMC evaluation costs.
SwISS improves scalability of Bayesian inference for large datasets.
Deep unfolding accelerates MCMC-based COP solvers.
Improves MCMC performance with adaptive affine transformations.
New bounds for SMC show its advantage over MCMC in multimodal distributions.
We propose a new algorithm to do posterior sampling of Kingman's coalescent, based upon the Particle Markov Chain Monte Carlo methodology. Specifically, the algorithm is an instantiation of the Particle Gibbs Sampling method, which alternately samples coalescent times conditioned on coalescent tree structures, and tree…
New methods improve efficiency of sampling algorithms for complex systems.
Introduces HMC method for sampling Gibbs densities.
The hybrid Monte Carlo (HMC) algorithm is used for Bayesian analysis of the generalized autoregressive conditional heteroscedasticity (GARCH) model. The HMC algorithm is one of Markov chain Monte Carlo (MCMC) algorithms and it updates all parameters at once. We demonstrate that how the HMC reproduces the GARCH paramete…
New MCMC method for complex models with large variables.
New samplers improve MCMC efficiency in high dimensions.
Markov Chain Monte Carlo methods have revolutionised mathematical computation and enabled statistical inference within many previously intractable models. In this context, Hamiltonian dynamics have been proposed as an efficient way of building chains which can explore probability densities efficiently. The method emerg…
We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC metho d itself. It turns out that autocorrelations between the data generated …
Recent developments in differentially private (DP) machine learning and DP Bayesian learning have enabled learning under strong privacy guarantees for the training data subjects. In this paper, we further extend the applicability of DP Bayesian learning by presenting the first general DP Markov chain Monte Carlo (MCMC)…
NP-iMCMC algorithm for nonparametric models in universal PPLs.
Enhances SMC² with Hessian info for more efficient posterior approximation.
Bayesian method learns network structure from Gaussian process priors.
We propose a Las Vegas transformation of Markov Chain Monte Carlo (MCMC) estimators of Restricted Boltzmann Machines (RBMs). We denote our approach Markov Chain Las Vegas (MCLV). MCLV gives statistical guarantees in exchange for random running times. MCLV uses a stopping set built from the training data and has maximum…
We introduce a new algorithm for approximate inference that combines reparametrization, Markov chain Monte Carlo and variational methods. We construct a very flexible implicit variational distribution synthesized by an arbitrary Markov chain Monte Carlo operation and a deterministic transformation that can be optimized…
New gradient estimator improves training for normalizing flows.
We introduce a gradient-based learning method to automatically adapt Markov chain Monte Carlo (MCMC) proposal distributions to intractable targets. We define a maximum entropy regularised objective function, referred to as generalised speed measure, which can be robustly optimised over the parameters of the proposal di…
Enhances sample diversity in SGMCMC for better uncertainty estimation in BNNs.