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Monte Carlo Scheme (monte + carlo_scheme)
Selected AbstractsAb initio calculations of intramolecular parameters for a class of arylamide polymersJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 6 2006Satyavani Vemparala Abstract Using DFT methods, we have determined intramolecular parameters for an important class of arylamide polymers displaying antimicrobial and anticoagulant inhibitory properties. A strong link has been established between these functions and the conformation that the polymers adopt in solution and at lipid bilayer interfaces. Thus, it is imperative for molecular dynamics simulations designed to probe the conformational behavior of these systems to accurately describe the torsional degrees of freedom. Standard force fields were shown to be deficient in this respect. Therefore, we have computed the relevant torsional energy profiles using a series of constrained geometry optimizations. We have also determined electrostatic parameters using our results in combination with standard RESP charge optimization. Force constants for bond and angle potentials were calculated by iteratively matching quantum and classical normal modes via a Monte Carlo scheme. The resulting new set of parameters accurately described the conformation and dynamical behavior of the arylamide polymers. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 693,700, 2006 [source] Volatility forecasting with double Markov switching GARCH modelsJOURNAL OF FORECASTING, Issue 8 2009Cathy W. S. Chen Abstract This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first-order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value-at-risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd. [source] Particle Markov chain Monte Carlo methodsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 3 2010Christophe Andrieu Summary., Markov chain Monte Carlo and sequential Monte Carlo methods have emerged as the two main tools to sample from high dimensional probability distributions. Although asymptotic convergence of Markov chain Monte Carlo algorithms is ensured under weak assumptions, the performance of these algorithms is unreliable when the proposal distributions that are used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to build efficient high dimensional proposal distributions by using sequential Monte Carlo methods. This allows us not only to improve over standard Markov chain Monte Carlo schemes but also to make Bayesian inference feasible for a large class of statistical models where this was not previously so. We demonstrate these algorithms on a non-linear state space model and a Lévy-driven stochastic volatility model. [source] SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELSAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2009M. P. Wand Summary Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian ,inference engines', such as bugs and vibes, facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology. [source] | ||||||||||