Collective proposal distributions for nonlinear MCMC samplers: mean-field theory and fast implementation
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Publication:2106803
DOI10.1214/22-EJS2091MaRDI QIDQ2106803
Grégoire Clarté, Jean Feydy, A. Diez
Publication date: 19 December 2022
Published in: Electronic Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1909.08988
Computational methods for problems pertaining to statistics (62-08) Software, source code, etc. for problems pertaining to statistics (62-04) Monte Carlo methods (65C05) Discrete-time Markov processes on general state spaces (60J05) Random number generation in numerical analysis (65C10) Stochastic particle methods (65C35)
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- Adaptive proposal distribution for random walk Metropolis algorithm
- Mean field limit and propagation of chaos for Vlasov systems with bounded forces
- Optimal scaling for the transient phase of Metropolis Hastings algorithms: the longtime behavior
- On the rate of convergence in Wasserstein distance of the empirical measure
- The Monge problem in \(\mathbb R^d\)
- Some mathematical models from population genetics. École d'Été de Probabilités de Saint-Flour XXXIX-2009
- Celebrating Cercignani's conjecture for the Boltzmann equation
- Geometric analysis for the Metropolis algorithm on Lipschitz domains
- On nonlinear Markov chain Monte Carlo
- Weighted Csiszár-Kullback-Pinsker inequalities and applications to transportation inequalities
- Convex Sobolev inequalities and spectral gap
- On adaptive Markov chain Monte Carlo algorithms
- Convergence of adaptive mixtures of importance sampling schemes
- Rapid solution of integral equations of classical potential theory
- Vlasov equations
- McKean-Vlasov Ito-Skorohod equations, and nonlinear diffusions with discrete jump sets
- Nonlinear diffusion with jumps
- Piecewise deterministic Markov processes for continuous-time Monte Carlo
- Cercignani's conjecture is sometimes true and always almost true
- CLTs and asymptotic variance of time-sampled Markov chains
- Safe adaptive importance sampling: a mixture approach
- Propagation of chaos and moderate interaction for a piecewise deterministic system of geometrically enriched particles
- Random batch methods (RBM) for interacting particle systems
- Optimal scaling for the transient phase of the random walk Metropolis algorithm: the mean-field limit
- Active particles, Volume 1. Advances in theory, models, and applications
- On Kac's chaos and related problems
- Mathematical Methods in Image Reconstruction
- Constructing optimal maps for Monge’s transport problem as a limit of strictly convex costs
- Entropy Methods for Diffusive Partial Differential Equations
- Mean Field Simulation for Monte Carlo Integration
- Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC
- A law of large numbers for moderately interacting diffusion processes
- Sequential Monte Carlo Samplers
- Markov Chains
- Particle Markov Chain Monte Carlo Methods
- Active Particles, Volume 2
- Equation of State Calculations by Fast Computing Machines
- Probabilistic Theory of Mean Field Games with Applications I
- Non-linear Markov Chain Monte Carlo
- A CLASS OF MARKOV PROCESSES ASSOCIATED WITH NONLINEAR PARABOLIC EQUATIONS
- Monte Carlo sampling methods using Markov chains and their applications
- The Monte Carlo Method
- A Model for Collision Processes in Gases. I. Small Amplitude Processes in Charged and Neutral One-Component Systems
- Optimal Transport
- A fast algorithm for particle simulations
- An adaptive Metropolis algorithm
- Algorithms for particle-field simulations with collisions
- Optimal scaling of MCMC beyond Metropolis
