Comparison Monte-Carlo and Langevin dynamics results
Sparse Regression Learning by Aggregation and Langevin Monte-Carlo
... (0.696) (0.806) (1.098) (0.907) (1.791) (1.063) Table 1: Average loss kb λ − λ ∗ k 2 of the estimators obtained by the EW-aggregate and the Lasso in Example 1. The standard deviation is given in parentheses. We note that ...
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Sparse regression learning by aggregation and Langevin Monte-Carlo
... numerical results on the Lasso in Table 1 are substantially different from those reported in the short version of this paper published in the Proceeding of COLT 2009 ...
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Analysis of Langevin Monte Carlo via Convex Optimization
... In this paper, we provide new insights on the Unadjusted Langevin Algorithm. We show that this method can be formulated as the first order optimization algorithm for an ob- jective functional defined on the ...
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Wavelet Monte Carlo dynamics
... A similar rough analysis for small particles is more difficult because of the more complex flow fields, while an accurate calculation would just be the long way to get to the simulation results. The important ...
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Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
... the results obtained with each method for the model d = 2, and by performing 100 repetitions to obtain 95% confidence ...the results for the first three coordinates of β β β ...
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Monte Carlo Comparison for Nonparametric Threshold Estimators
... For the IDKE, our results show several features. Firstly, the IDKE is affected by the position of the actual threshold value. The influence is not as substantial as the DKE. Indeed, the integration allows more ...
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True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison
... theoretical results on how a spurious LM volatility process affects the elasticity of the stock market price with respect to volatility; we also showed that spurious persistence in the data can be the effect of ...
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Some comments on Monte Carlo and molecular dynamics methods
... molecular dynamics steps. The exact results are well reproduced, confirming the consistency between the measured microcanonical entropy function S(U ) and its derivative (whether numerical or ...
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Applications of Monte Carlo methods in studying polymer dynamics
... 4.4.2 Monte Carlo Methods to Approximate Marginal Like- lihood Monte Carlo methods are widely used approaches to approximate probability distri- bution that is hard to evaluate or sample ...of ...
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Monte Carlo analysis of incomplete paired-comparison experiments
... Monte Carlo Analysis of Incomplete Paired-Comparison Experiments Westland, Li and Cheung, Journal of Imaging Science and Technology (2015) randomly selected from the complete set of possible ...The ...
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Bayesian model comparison via sequential Monte Carlo
... substantial attention, information available in posterior distributions of any given model does not characterize modes that exist only in models of higher dimension; and thus a successful between-model move between these ...
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A Monte Carlo Comparison of Robust MANOVA Test Statistics
... rates compared to their non-trimmed counterparts. However, these differences were consistently very small, and generally did not offer a substantive advantage over the non-trimmed test statistics. Note that power for all ...
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A Monte Carlo comparison of Bayesian testing for cointegration rank
... Table 1 summarizes the results of Monte Carlo simulation for two-variable VECMs (n = 2) by computing the BIC and the Chib’s method. Table 2 reports the results when n = 3. Each value in the ...
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Higher order quasi-Monte Carlo methods: A comparison
... However, higher order nets using the explicit construction come very close in our low-dimensional tests. It is probably interesting to keep a close eye on further developments of higher order digital nets as they clearly ...
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Equilibrium Structure of Electrolyte calculated using Equilibrium Monte Carlo, Molecular Dynamics, and Boltzmann Transport Monte Carlo Simulations
... anion g +- (r) pair correlation functions computed using the three different simulation techniques are shown in Figure 1. The comparison between all three simulations is excellent. For this result the BTMC ...
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A comparison of nonlinear population Monte Carlo and particle Markov chain Monte Carlo algorithms for Bayesian inference in stochastic kinetic models
... – more importantly, the analysis relies on the ability to compute the non-normalized IWs exactly. It is apparent from the algorithm description in Section 4 that, in the case of the SKM models of interest in this paper, ...
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Variational path integral molecular dynamics and hybrid Monte Carlo algorithms
... M / β~. When using the fourth order decomposition, the parameter γ was chosen to be 0. The variational parameters α = 0.1 and 0.7 were tested. The variational energies are given to be 1.3 and 0.53 for α = 0.1 and 0.7, ...
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Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers.
... SIR methods, including BF, resample the particles at each time step to filter out particles corresponding to unlikely posterior probabilities p θ (x t |y 1:t ). Although this helps SIR to keep only particles within the ...
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Reconfigurable computing for Monte Carlo simulations: results and prospects of the Janus project
... physics results that we have obtained in approximately 4 years operating with this ...spin-glass dynamics or to thermalize large systems at low tem- peratures, thus gaining access to new ...
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Testing for (non)linearity in economic time series: a Monte Carlo comparison
... The only test that exhibits large power both for ARCH/GARCH and TAR DGPs is the BDS, though the sample size should be larger than 500. In case of MS models, the performance of the tests changes. One could expect the ...
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