Search results for "Metropolis–Hastings algorithm"
showing 10 items of 14 documents
Distributed Particle Metropolis-Hastings Schemes
2018
We introduce a Particle Metropolis-Hastings algorithm driven by several parallel particle filters. The communication with the central node requires the transmission of only a set of weighted samples, one per filter. Furthermore, the marginal version of the previous scheme, called Distributed Particle Marginal Metropolis-Hastings (DPMMH) method, is also presented. DPMMH can be used for making inference on both a dynamical and static variable of interest. The ergodicity is guaranteed, and numerical simulations show the advantages of the novel schemes.
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling
2011
Abstract Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessm…
Grapham: Graphical models with adaptive random walk Metropolis algorithms
2008
Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation covering several such methods, with emphasis on graphical models for directed acyclic graphs. The implemented algorithms include the seminal Adaptive Metropolis algorithm adjusting the proposal covariance according to the history of the chain and a Metropolis algorithm adjusting the proposal scale based on the observed acceptance probability. Different variants of the algorithms allow one, for example, to use these two algorithms together, employ delayed rejection and adjust several parameters of the algorithm…
Anti-tempered Layered Adaptive Importance Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference in signal processing, machine learning and statistics. In this work, we introduce an adaptive importance sampler which mixes together the benefits of the Importance Sampling (IS) and Markov Chain Monte Carlo (MCMC) approaches. Different parallel MCMC chains provide the location parameters of the proposal probability density functions (pdfs) used in an IS method. The MCMC algorithms consider a tempered version of the posterior distribution as invariant density. We also provide an exhaustive theoretical support explaining why, in the presented technique, even an anti-tempering strategy (reducing the scaling of the posterior) can …
Close packing of clusters: Application toAl100
2003
The lowest energy configurations of close-packed clusters up to N=110 atoms with stacking faults are studied using the Monte Carlo method with Metropolis algorithm. Two types of contact interactions, a pair-potential and a many-atom interaction, are used. Enhanced stability is shown for N=12, 26, 38, 50, 59, 61, 68, 75, 79, 86, 100 and 102, of which only the sizes 38, 75, 79, 86, and 102 are pure FCC clusters, the others having stacking faults. A connection between the model potential and density functional calculations is studied in the case of Al_100. The density functional calculations are consistent with the experimental fact that there exist epitaxially grown FCC clusters starting from…
Monte Carlo simulation of correlated electrons in disordered systems
1992
Abstract The properties of many-electron states in disordered systems with long-range electron-eletron interaction are investigated by means of a Monte Carlo simulation. Using the Metropolis algorithm, three-dimensional systems up to 512 sites are systematically analysed. The low-lying excitations are investigated in order to distinguish between one-particle and many-particle hopping. In the interesting regime in which disorder and correlation effects are equally important we find that variable-range hopping is insignificant for electron transfer when compared with the contribution from nearest-neighbour one-electron hopping processes as well as variable-number hopping.
Monte Carlo simulation of crystalline polyethylene
1996
Abstract We consider here the problem of constructing an efficient algorithm for a classical Monte Carlo simulation of crystalline polyethylene with unconstrained bond lengths and angles. This macromolecular crystal presents a particular example of a system with many different energy scales, ranging from soft ones represented by nonbonded van der Waals interactions, to stiff ones, represented in particular by bond stretching. A proper sampling of all the energy scales poses a problem and it is shown that a standard Metropolis algorithm employing just local moves is not very efficient at low temperatures. As a solution it is proposed to employ also global moves consisting of displacements of…
Markov Chain Monte Carlo Methods for High Dimensional Inversion in Remote Sensing
2004
SummaryWe discuss the inversion of the gas profiles (ozone, NO3, NO2, aerosols and neutral density) in the upper atmosphere from the spectral occultation measurements. The data are produced by the ‘Global ozone monitoring of occultation of stars’ instrument on board the Envisat satellite that was launched in March 2002. The instrument measures the attenuation of light spectra at various horizontal paths from about 100 km down to 10–20 km. The new feature is that these data allow the inversion of the gas concentration height profiles. A short introduction is given to the present operational data management procedure with examples of the first real data inversion. Several solution options for…
MCMC methods to approximate conditional predictive distributions
2006
Sampling from conditional distributions is a problem often encountered in statistics when inferences are based on conditional distributions which are not of closed-form. Several Markov chain Monte Carlo (MCMC) algorithms to simulate from them are proposed. Potential problems are pointed out and some suitable modifications are suggested. Approximations based on conditioning sets are also explored. The issues are illustrated within a specific statistical tool for Bayesian model checking, and compared in an example. An example in frequentist conditional testing is also given.