Monte Carlo strategies in scientific computing

By Jun S. Liu. 2001

2,320 citations recorded by the source corpus.

Subfields: Numerical Methods & Scientific Computing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Numerical Methods & Scientific Computing

Citations in the corpus

Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning (2020, methodology): “Note that the normalization of w may be ensured by techniques such as self-normalized importance sampling (Liu, 2001).”
Batch Stationary Distribution Estimation (2020, background): “…demonstrate its applicability and efﬁcacy in a range of important scenarios beyond queueing, including: • Stochastic differential equations (SDEs) SDEs are an essential modeling tool in many ﬁelds like statistical physics (Kadanoff, 2000), ﬁnance (Oksendal, 2013) and molecular dynamcis (Liu, 2001).”
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation (2019, methodology): “The second approach is to use importance sampling to correct the sampling bias in off-policy data, so that an (almost) unbiased estimator can be obtained (Liu, 2001; Strehl et al., 2010; Bottou et al., 2013).”
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation (2018, methodology): “Most state-of-the-art off-policy estimation methods are based on importance sampling (IS) [e.g. , 22].”
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018, methodology): “The simplest method for tackling the UP problem is the Monte Carlo (MC) method [3, 4, 5].”
Uncertainty propagation in CFD using polynomial chaos decomposition (2006, methodology): “The simplest of these methods is the MC approach (see e.g. Liu, 2001), which relies on an unbiased sampling of the random parameter space.”
Use of exchangeable pairs in the analysis of simulations (2004, background): “Fishman [9] and Liu [17] give current accounts of a host of other methods for constructing reversible Markov chains.”
An adaptive sequential Monte Carlo method for approximate Bayesian computation (2011, methodology): “The rest of this article is organized as follows.”
Structured Learning and Prediction in Computer Vision (2011, methodology): “The Metropolis-Hastings method is so general that many variations have been proposed; for an in-depth review see [Liu, 2001].”
A Latent Cluster-Mean Approach to the Contextual Effects Model With Missing Data (2010, methodology): “The conditional mean can be computed by a Bayesian approach specifying the joint prior distribution of v|/cc,acc, and yc and integrating over these using Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampler and Metropolis Hastings algorithm (Gelfand & Smith, 1990; Gelman et al., 1995; Gilks”
A new analysis of experimental data on olivine rheology (2008, methodology): “There are a number of excellent articles and books on MCMC methods [e.g., Geyer, 1992; Smith and Roberts, 1993; Gilks et al., 1996; Gamerman, 1997; Liu, 2001; Robert and Cassela, 2004], and readers are referred to those references for formal and rigorous explanations.”
Off-policy learning based on weighted importance sampling with linear computational complexity (2015, background): “…importance sampling (WIS), which often gives much lower variance than the ordinary form of importance sampling and is generally preferred due to its superior empirical performance (Hester-berg 1988, Precup, Sutton & Singh 2000, Shelton 2001, Liu 2001, Robert & Casella 2004, Koller & Friedman 2009).”
Applied Stochastic Differential Equations (2019, neutral): “, 2013; Särkkä, 2013), but MCMC methods have a multitude of other applications as well (Liu, 2001; Brooks et al., 2011).”
Adaptive sequential Monte Carlo by means of mixture of experts (2011, methodology): “…a tool for solving spe-ciﬁc problems, such as the optimal ﬁltering problem in general state-space models or simulation of rare events (these topics are abundantly covered in Liu 2001; Doucet et al. 2001; Ristic et al. 2004; Cappé et al. 2005; Del Moral et al. 2006, and the references therein).”
An algorithm for finding protein–DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments (2002, methodology): “rely on iterative procedures (either expectation maximization or Gibbs sampling) with nonquantifiable convergence propertie”
The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method (2015, background): “Many MCMC schemes available at present are based on the Metropolis-Hastings (MH) algorithm and their eﬃciency is strongly dependent on the ability of the user to design proposal distributions capturing the main features of the target distribution; see [20] for a comprehensive review.”