Search results for " Monte Carlo"
showing 10 items of 400 documents
Critical phenomena without “hyper scaling”: How is the finite-size scaling analysis of Monte Carlo data affected?
2010
Abstract The finite size scaling analysis of Monte Carlo data is discussed for two models for which hyperscaling is violated: (i) the random field Ising model (using a model for a colloid-polymer mixture in a random matrix as a representative) (ii) The Ising bi-pyramid in computing surface fields.
Path-integral Monte Carlo study of crystalline Lennard-Jones systems.
1995
The capability of the path-integral Monte Carlo (PIMC) method to describe thermodynamic and structural properties of solids at low temperatures is studied in detail, considering the noble-gas crystals as examples. In order to reduce the systematic limitations due to finite Trotter number and finite particle number we propose a combined Trotter and finite-size scaling. As a special application of the PIMC method we investigate $^{40}\mathrm{Ar}$ at constant volume and in the harmonic approximation. Furthermore, isotope effects in the lattice constant of $^{20}\mathrm{Ne}$ and $^{22}\mathrm{Ne}$ are computed at zero pressure. The obtained results are compared with classical Monte Carlo result…
Cluster Expansions and Variational Monte Carlo in Medium Light Nuclei
1993
The B1 Brink-Boeker effective interaction is used to compute variational upper bounds for the ground state energy of nuclei from 16 O up to 40 Ca. The calculations are carried out by means of the Variational Monte Carlo method and with a multiplicative cluster expansion up to fourth order.
Path integral Monte Carlo study of the internal quantum state dynamics of a generic model fluid
1996
We study the quantum dynamics of a generic model fluid with internal quantum states and classical translational degrees of freedom in two spatial dimensions. The path integral Monte Carlo data for the imaginary time correlation functions are presented and analyzed by the maximum entropy method. A comparison of the frequency distribution with those of a mean field approximation and virial expansion shows good agreement at high and low densities, respectively. \textcopyright{} 1996 The American Physical Society.
Molecular-Level Characterization of Heterogeneous Catalytic Systems by Algorithmic Time Dependent Monte Carlo
2009
Monte Carlo algorithms and codes, used to study heterogeneous catalytic systems in the frame of the computational section of the NANOCAT project, are presented along with some exemplifying applications and results. In particular, time dependent Monte Carlo methods supported by high level quantum chemical information employed in the field of heterogeneous catalysis are focused. Technical details of the present algorithmic Monte Carlo development as well as possible evolution aimed at a deeper interrelationship of quantum and stochastic methods are discussed, pointing to two different aspects: the thermal-effect involvement and the three-dimensional catalytic matrix simulation. As topical app…
Sequential Monte Carlo Methods in Random Intercept Models for Longitudinal Data
2017
Longitudinal modelling is common in the field of Biostatistical research. In some studies, it becomes mandatory to update posterior distributions based on new data in order to perform inferential process on-line. In such situations, the use of posterior distribution as the prior distribution in the new application of the Bayes’ theorem is sensible. However, the analytic form of the posterior distribution is not always available and we only have an approximated sample of it, thus making the process “not-so-easy”. Equivalent inferences could be obtained through a Bayesian inferential process based on the set that integrates the old and new data. Nevertheless, this is not always a real alterna…
Micropollutants throughout an integrated urban drainage model: Sensitivity and uncertainty analysis
2017
Abstract The paper presents the sensitivity and uncertainty analysis of an integrated urban drainage model which includes micropollutants. Specifically, a bespoke integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole – SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Overall, five scenarios, each characterized by different uncertainty combinations of sub-systems (i.e…
Uncertainty in sewer sediment deposit modelling: detailed vs simplified modelling approaches.
2012
Abstract The paper presents the results of a study in which the uncertainty levels associated with a detailed and a simplified/parsimonious sewer sediment modelling approach have been compared. The detailed approach used an Infoworks CS sewer network model combined with a user developed sediment transport code and the simplified approach used a conceptual sewer flow and quality model. The two approaches have been applied to a single case study sewer network and the simulation results compared. The case study was selected as moderate storm events had occurred during a 2 year rainfall and sewer flow monitoring period. Flooding had been observed and this was thought to be caused by significant…
Geographical variation in pharmacological prescription
2009
Promoting rational drug administration in treatments is one of the most important issues in Public Health. Bayesian hierarchical models are a very useful tool for incorporating geographical information into the analysis of pharmacological prescription data. They allow the mapping of spatial components which express the trend of geographical variation. In addition, these models are able to deal with uncertainty in a sequential way through prior distributions on parameters and hyperparameters. Bayes' theorem combines all types of information and provides the posterior distribution which is computed through Markov Chain Monte Carlo (MCMC) simulation methods. Simulated data for pharmacological …
Primordial power spectrum features in phenomenological descriptions of inflation
2016
We extend an alternative, phenomenological approach to inflation by means of an equation of state and a sound speed, both of them functions of the number of $e$-folds and four phenomenological parameters. This approach captures a number of possible inflationary models, including those with non-canonical kinetic terms or scale-dependent non-gaussianities. We perform Markov Chain Monte Carlo analyses using the latest cosmological publicly available measurements, which include Cosmic Microwave Background (CMB) data from the Planck satellite. Within this parametrization, we discard scale invariance with a significance of about $10\sigma$, and the running of the spectral index is constrained as …