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Lattice Gaussian Sampling by Markov Chain Monte Carlo: Bounded Distance Decoding and Trapdoor Sampling
lattice Gaussian sampling Markov chain Monte Carlo bounded distance decoding
2019/6/5
Sampling from the lattice Gaussian distribution plays an important role in various research fields. In this paper, the Markov chain Monte Carlo (MCMC)-based sampling technique is advanced in several f...
Generative Models and Markov Chain Monte Carlo Techniques for Detection and Reconstruction of Stairs from 2D Point Clouds
Building Stairs Detection Reconstruction Markov Chain Monte Carlo MAP Estimation Generative Model Point Cloud
2015/10/12
The paper describes an approach for the automatical reconstruction of homogeneous straight stairs from point cloud data by using a
generative model and Markov Chain Monte Carlo techniques for estima...
The Markov Chain Monte Carlo Revolution。
Some things we’ve learned (about Markov chain Monte Carlo)
Markov chains nonreversible chains rates of convergence
2015/7/7
This paper offers a personal review of some things we’ve learned about rates of convergence of Markov chains to their stationary distributions. The main topic is ways of speeding up diffusive behavior...
de Finetti Priors using Markov chain Monte Carlo computations
Priors MCMC Contingency Tables Bayesian Inference Independence
2015/7/7
de Finetti Priors using Markov chain Monte Carlo computations。
Coupled coarse graining and Markov Chain Monte Carlo for lattice systems
Markov chain monte carlo random lattice model the short-range particles energy
2014/12/24
We propose an efficient Markov Chain Monte Carlo method for sampling equilibrium distributions for stochastic lattice models, capable of handling correctly long and short-range particle interactions. ...
Computing the Bayes Factor from a Markov chain Monte Carlo Simulation of the Posterior Distribution
Markov chain Bayes Factor
2014/12/22
Computation of the marginal likelihood from a simulated posterior distribution is central to Bayesian model selection but is computationally difficult. I argue that the marginal likelihood can be reli...
Inference in Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
Inference Kingman's Coalescent with Particle Markov Chain Monte Carlo Method
2013/6/13
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 Partic...
Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices
Inserting Vortices Markov Chain Monte-Carlo Asymptotic Performance
2012/11/23
We present a new way of converting a reversible finite Markov chain into a non-reversible one, with a theoretical guarantee that the asymptotic variance of the MCMC estimator based on the non-reversib...
Rao-Blackwellised Interacting Markov Chain Monte Carlo for Electromagnetic Scattering Inversion
Rao-Blackwellised Markov Chain Monte Carlo Electromagnetic Scattering Inversion
2012/11/22
The following electromagnetism (EM) inverse problem is addressed. It consists in estimating local radioelectric properties of materials recovering an object from the global EM scattering measurement, ...
Adaptive Markov Chain Monte Carlo confidence intervals
Adaptive Markov Chain Monte Carlo confidence intervals
2012/11/22
In Adaptive Markov Chain Monte Carlo (AMCMC) simulation, classical estimators of asymptotic variances are inconsistent in general. In this work we establish that despite this inconsistency, confidence...
Adaptive Markov Chain Monte Carlo for Auxiliary Variable Method and Its Application to Parallel Tempering
Adaptive Markov Chain Monte Carlo Auxiliary Variable Method Parallel Tempering Conver-gence
2012/9/19
Auxiliary variable methods such as the Parallel Tempering and the cluster Monte Carlo methods generate samples that follow a target distri-bution by using proposal and auxiliary distributions.In sampl...
On nonlinear Markov chain Monte Carlo
Foster–Lyapunov condition interacting Markov chains nonlinear Markov kernels
2011/7/19
Let $\mathscr{P}(E)$ be the space of probability measures on a measurable space $(E,\mathcal{E})$. In this paper we introduce a class of nonlinear Markov chain Monte Carlo (MCMC) methods for simulatin...
On nonlinear Markov chain Monte Carlo
Foster–Lyapunov condition interacting Markov chains nonlinear Markov kernels Poisson equation
2011/9/9
Abstract: Let $\mathscr{P}(E)$ be the space of probability measures on a measurable space $(E,\mathcal{E})$. In this paper we introduce a class of nonlinear Markov chain Monte Carlo (MCMC) methods for...
Markov Chain Monte Carlo Based on Deterministic Transformations
Geostatistics High dimension Inverse transfromation Jacobian
2011/7/6
In this article we propose a novel MCMC method based on deterministic transformations T : X x D --> X where X is the state-space and D is some set which may or may not be a subset of X. We refer to ou...