Monte Carlo algorithm
Optimal tuning of the hybrid Monte Carlo algorithm
... 5 Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK. E-mail: [email protected] We investigate the properties of the hybrid Monte Carlo algorithm (HMC) in high dimensions. HMC ...
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Bayes and Big Data: The Consensus Monte Carlo Algorithm
... The largest disagreement is on AdFormat 6, which only one of the 5 shards had any information about. There was one shard that exhibited slow mixing on the AdFormat 5 coefficient, leading to a somewhat overdispersed ...
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Implementation and analysis of an adaptive multilevel Monte Carlo algorithm
... multilevel Monte Carlo algorithm, where the multi- level simulations are performed on adaptively generated mesh hierarchies based on computable a posteriori weak error ...adaptive algorithm ...
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CHM 579 Lab 1: Basic Monte Carlo Algorithm
... Lab Procedure: Before starting with the procedure below, make sure that you know basic things about the Linux command line environment. You have 15 files that contain an implementation of the Monte-Carlo ...
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Quasi Monte Carlo algorithm for computing smallest and largest generalised eigenvalues
... quasi Monte Carlo algorithm is ...with Monte Carlo and quasi Monte Carlo methods for evaluating extremal eigenvalue of real ...quasi Monte Carlo ...
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Joint Haplotype Assembly and Genotype Calling via Sequential Monte Carlo Algorithm
... assembly algorithm, ParticleHap, that relies on a probabilistic description of the sequencing data to jointly infer genotypes and assemble the most likely ...sequential Monte Carlo algorithm ...
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An Investigation on the Holddown Margin using Monte-Carlo Algorithm for the PWR Fuel Assembly
... ABSTRACT The holddown springs provide an acceptable holddown force against hydraulic uplift force absorbing the length change of the fuel assembly relative to the space between the upper and lower core plates in PWR. ...
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Combining Strategies for Parallel Stochastic Approximation Monte Carlo Algorithm of Big Data
... MCMC algorithm in each divided data ...in Monte Carlo algorithm (SAMC), a very sophisticated algorithm in theory and applications, can avoid getting trapped into local optima and ...
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Bayesian Logistic Regression Modelling via Markov Chain Monte Carlo Algorithm
... Chain Monte Carlo algorithm offers an alternative framework for estimating the logistic regression ...MCMC algorithm to logistic regression ...
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Optimal tuning of the hybrid Monte Carlo algorithm
... state space. In contrast, and as discussed in the following sections, HMC exploits the information on the derivative of the log density to deliver guided, global moves, with higher acceptance probability. HMC is closely ...
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A Monte Carlo Algorithm for State and Parameter Estimation of Extended Targets
... developed algorithm is applied to a ship, whose shape is modelled by an ...sampling algorithm with finite mixtures is proposed for the evaluation of the extent parameters whereas a subop- timal Bayesian ...
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Markov chain monte carlo algorithm for bayesian policy search
... 5.2 Application of MCMC Method to a Nonlinear Model of an Inverted Pendulum Given a nonlinear model of a continuous MDP which is here an Inverted Pen- dulum, objective is the stabilization problem of the Inverted ...
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Theory and Application of a Pure-sampling Quantum Monte Carlo Algorithm
... quantum Monte Carlo is to calculate physical prop- erties that are independent of the importance sampling function being employed in the calculation, save for the mismatch of its nodal hypersurface with ...
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A sequential Monte Carlo algorithm for inference of subclonal structure in cancer
... Application to solid tumor datasets Data pre-processing. The somatic mutation data of real solid tumors come from the American Association for Cancer Research (AACR) Genomics Evidence Neoplasia Informa- tion Exchange ...
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MATHICSE Technical Report : A continuation multilevel Monte Carlo algorithm
... Level Monte Carlo (CMLMC) al- gorithm for weak approximation of stochastic models that are described in terms of differential equations either driven by random measures or with random ...CMLMC ...
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A Markov chain Monte Carlo algorithm for multiple imputation in large surveys
... chain Monte Carlo technique that is used for the algorithm developed in this paper is similar to the method presented by Schafer (1997), who used smaller data sets with only few conditioning ...
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A multilevel Monte Carlo algorithm for Lévy-driven stochastic differential equations
... multilevel Monte Carlo algorithms for the computation of S ( f ) with a focus on path dependent f ’s that are Lipschitz functions on the space D[0 , 1] with the supremum ...
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Patient-oriented simulation based on Monte Carlo algorithm by using MRI data
... Therefore, we offer a systematic approach for 3D brain modeling based on image segmentation process with in vivo MRI T1 three-dimensional image. For investigation of individualized difference in brain structure with ...
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Four essays on sequential Monte Carlo and quasi-Monte Carlo methods
... sequential Monte Carlo ...the algorithm is discussed and the elicitation of the prior distribu- tions takes into consideration some unusual behaviour of the likelihood function and the corresponding ...
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Monte Carlo Computability
... 4. Nash is the problem that maps a bi-matrix game (A, B) ∈ R m×n × R m×n to one of its Nash equilibria [23] and is Las Vegas computable [8]. 5. Sorting stands for the problem SORT 2 : 2 N → 2 N of sorting a binary ...
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