arXiv research

A new method combines AIS and SMCI for efficient evaluation of Ising models.

Combines normalizing flows and quasi-Monte Carlo for improved numerical integration.

New Hamiltonian Monte Carlo method for non-canonical dynamics.

This study compares MC and QMC methods for likelihood functions.

Tensor networks improve integration accuracy for high-dimensional problems.

New method uses Coulomb gases for Monte Carlo integration with reduced errors.

We use neural networks as control variates with geometric integration techniques.

Improved efficiency in HMC samplers reduces dissipative behavior.

Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e…

The paper uses Fourier integral theorem for estimating multivariate distributions.

Simplified uHMC with time integration improves accuracy and efficiency.

VegasFlow accelerates complex simulations across various hardware platforms.

The paper integrates multiple Gaussian process predictions using Monte Carlo sampling.

New integral theorems improve density function estimations.

New method samples from time-integrated stochastic bridges using neural networks.

Quantum speedup for Monte Carlo integration reduces integrand calls.

New method controls bias in unadjusted Hamiltonian Monte Carlo and underdamped Langevin.

In this paper we propose and study a family of continuous wavelets on general domains, and a corresponding stochastic discretization that we call Monte Carlo wavelets. First, using tools from the theory of reproducing kernel Hilbert spaces and associated integral operators, we define a family of continuous wavelets by …

Gaussian process is a very promising novel technology that has been applied to both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification problem, because we encounter intractable integrals. In this paper we …

TQ separates sampling and integration for high-dimensional integrals.

We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians. We show its relevance to Riemannian manifold Hamiltonian Monte Carlo (RMHMC) and provide an alternative to the currently used generalised leapfrog symplectic integrator, which relies on …

New method improves self-supervised representation learning using probabilistic modeling and Monte Carlo integration.

Generalizes SMCI to improve Boltzmann machine learning accuracy.

We discuss a semi-analytical method for solving SABR-type equations based on path integrals. In this approach, one set of variables is integrated analytically while the second set is integrated numerically via Monte-Carlo. This method, known in the literature as Conditional Monte-Carlo, leads to compact expressions fun…

The paper introduces new estimators for multivariate functions using Fourier methods.

The paper improves probabilistic herding methods using Gibbs distributions.

New algorithm speeds up MCMC for complex distributions.

We consider the problem of improving the efficiency of randomized Fourier feature maps to accelerate training and testing speed of kernel methods on large datasets. These approximate feature maps arise as Monte Carlo approximations to integral representations of shift-invariant kernel functions (e.g., Gaussian kernel).…

The standard Kernel Quadrature method for numerical integration with random point sets (also called Bayesian Monte Carlo) is known to converge in root mean square error at a rate determined by the ratio $s/d$, where $s$ and $d$ encode the smoothness and dimension of the integrand. However, an empirical investigation re…

Adaptive HMC improves sampling efficiency by optimizing mass matrix.

Improves accuracy of SMCI estimators without expanding sum regions.

MCNO learns PDE solution operators using Monte Carlo sampling.

This paper introduces repulsive Monte Carlo methods for computing the sliced Wasserstein distance.

Consider a process, stochastic or deterministic, obtained by using a numerical integration scheme, or from Monte-Carlo methods involving an approximation to an integral, or a Newton-Raphson iteration to approximate the root of an equation. We will assume that we can sample from the distribution of the process from time…

Corrects errors in ILA for Bayesian inference in LGMs.

Develops a neural surrogate for proton dose calculation using Monte Carlo dropout uncertainty.

NCV uses neural networks to improve Monte Carlo integration.

Posterior refinement improves sample efficiency in Bayesian neural networks.

Quantum computing techniques applied to Monte Carlo simulations in finance.

This paper develops a new framework to assess crypto portfolio risk using simulation methods.

The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integr…

New method improves sampling efficiency for complex distributions.

RHMC accelerates sampling from log-concave distributions.

Normal distributions ensure asymptotic variance reduction in moment matching Monte Carlo.

Magnetic manifold HMC improves sampling on constrained manifolds.

Enhances option pricing for American-style options using JDOI method.

TSAW improves MCMC integral estimation with faster convergence.

Proposes a new simulator for complex arrival processes.