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Found 10 papers, 7 papers with code
Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections
The Sliced-Wasserstein distance (SW) is being increasingly used in machine learning applications as an alternative to the Wasserstein distance and offers significant computational and statistical benefits.
Sequential Monte Carlo algorithms for agent-based models of disease transmission
Agent-based models of disease transmission involve stochastic rules that specify how a number of individuals would infect one another, recover or be removed from the population.
Computation Cellular Automata and Lattice Gases Populations and Evolution Methodology
Schrödinger Bridge Samplers
This is achieved by iteratively modifying the transition kernels of the reference Markov chain to obtain a process whose marginal distribution at time $T$ becomes closer to $\pi_T = \pi$, via regression-based approximations of the corresponding iterative proportional fitting recursion.
Estimating Convergence of Markov chains with L-Lag Couplings
Markov chain Monte Carlo (MCMC) methods generate samples that are asymptotically distributed from a target distribution of interest as the number of iterations goes to infinity.
Computation Methodology
Unbiased Smoothing using Particle Independent Metropolis-Hastings
We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements.
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach
We propose a general statistical framework for clustering multiple time series that exhibit nonlinear dynamics into an a-priori-unknown number of sub-groups.
Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation
Posterior distributions often feature intractable normalizing constants, called marginal likelihoods or evidence, that are useful for model comparison via Bayes factors.
Computation Methodology
Unbiased Hamiltonian Monte Carlo with couplings
We propose a methodology to parallelize Hamiltonian Monte Carlo estimators.
Computation
Unbiased Markov chain Monte Carlo with couplings
Markov chain Monte Carlo (MCMC) methods provide consistent approximations of integrals as the number of iterations goes to infinity.
Methodology Computation
Smoothing with Couplings of Conditional Particle Filters
The method combines two recent breakthroughs: the first is a generic debiasing technique for Markov chains due to Rhee and Glynn, and the second is the introduction of a uniformly ergodic Markov chain for smoothing, the conditional particle filter of Andrieu, Doucet and Holenstein.
Methodology Computation
