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A Markov Chain Perspective on Adaptive Monte Carlo Algorithms
Markov Chain Perspective Adaptive Monte Carlo Algorithms
2015/7/8
This paper discusses some connections between adaptive Monte Carlo algorithms and general state space Markov chains. Adaptive algorithms are iterative methods in which previously generated samples are...
ERROR ANALYSIS OF COARSE-GRAINED KINETIC MONTE CARLO METHOD
Coarse grain kinetic monte carlo simulation grid the stochastic dynamics structural model
2014/12/25
In this paper we investigate the approximation properties of the coarse-graining procedure applied to kinetic Monte Carlo simulations of lattice stochastic dynamics. We provide both analytical and num...
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. ...
An Adaptive Sequential Monte Carlo Algorithm for Computing Permanents
Sequential Monte Carlo Permanents Relative Variance
2013/6/14
We consider the computation of the permanent of a binary n by n matrix. It is well- known that the exact computation is a #P complete problem. A variety of Markov chain Monte Carlo (MCMC) computationa...
Bayesian Multi-Dipole Modeling of Single MEG Topographies by Adaptive Sequential Monte Carlo Samplers
Magnetoencephalography inverse problem Multi-object estimation Multi-dipole models Adaptive Sequential Monte Carlo samplers
2013/6/14
We describe a novel Bayesian approach to the estimation of neural currents from a single distribution of magnetic field, measured by magnetoencephalography. We model neural currents as an unknown numb...
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...
Statistical inference for Sobol pick freeze Monte Carlo method
Statistical inference Sobol pick freeze Monte Carlo method
2013/4/28
Many mathematical models involve input parameters, which are not precisely known. Global sensitivity analysis aims to identify the parameters whose uncertainty has the largest impact on the variabilit...
Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach
Adaptive Monte Carlo algorithms Bayesian model comparison Normalising constants Path sampling Thermodynamic integration
2013/4/27
Model comparison for the purposes of selection, averaging and validation is a problem found throughout statistics and related disciplines. Within the Bayesian paradigm, these problems all require the ...
Toward Optimal Stratification for Stratified Monte-Carlo Integration
Toward Optimal Stratification Stratified Monte-Carlo Integration
2013/4/27
We consider the problem of adaptive stratified sampling for Monte Carlo integration of a noisy function, given a finite budget n of noisy evaluations to the function. We tackle in this paper the probl...
Discrepancy bounds for uniformly ergodic Markov chain quasi-Monte Carlo
Information visualization Formal Concept Analysis Galois sub-hierarchy
2013/4/27
In [Chen, D., Owen, Ann. Stat., 39, 673--701, 2011] Markov chain Monte Carlo (MCMC) was studied under the assumption that the driver sequence is a deterministic sequence rather than independent U(0,1)...
Monte-Carlo utility estimates for Bayesian reinforcement learning
Monte-Carlo estimates Bayesian reinforcement learning
2013/5/2
This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct ...
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...
MMCTest - A Safe Algorithm for Implementing Multiple Monte Carlo Tests
Bootstrap/resampling Computationally Intensive Methods Multiple Comparisons False Discovery Rate Sequential Algorithm
2012/11/22
We are interested in testing multiple hypotheses using tests that can only be evaluated by simulation such as permutation tests or bootstrap tests. This article introduces a sequential algorithm which...
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...
Toward Practical N2 Monte Carlo: the Marginal Particle Filter
Practical N2 Monte Carlo Marginal Particle Filter
2012/9/19
Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dy-namic models. These methods allow us to ap-proximate the joint posterior distribution using sequential ...