Search results for "Metropolis"
showing 10 items of 43 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…
Impacts of COVID-19 and pandemic control measures on public transport ridership in European urban areas – The cases of Vienna, Innsbruck, Oslo, and A…
2021
The study uses the case of two regions with small and medium sized cities (Agder in Norway and the greater Innsbruck area in Austria) and two European capitals, Vienna and Oslo, to showcase the impact of the COVID-19 pandemic on public transport ridership in northern and central Europe. The comprehensive timeline of actions taken by governments and public transport providers in Austria and Norway, and their impact on public transport ridership in the first and second waves of the pandemic form the basis of a descriptive study. Comparing the data, a strong negative impact on the public transport patronage in the first wave of the pandemic was found, despite a comparable low number of cases p…
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…
Group Importance Sampling for particle filtering and MCMC
2018
Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in different works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discus…
A Review of Multiple Try MCMC algorithms for Signal Processing
2018
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data. More specifically, Bayesian inference needs the computation of {\it a-posteriori} estimators which are often expressed as complicated multi-dimensional integrals. Unfortunately, analytical expressions for these estimators cannot be found in most real-world applications, and Monte Carlo methods are the only feasible approach. A very powerful class of Monte Carlo techniques is formed by the Markov Chain Monte Carlo (MCMC) algorithms. They generate a Markov chain such that its stationary distribution coincides with the target posterior density. In this work, we perform a t…
Adaptive independent sticky MCMC algorithms
2018
In this work, we introduce a novel class of adaptive Monte Carlo methods, called adaptive independent sticky MCMC algorithms, for efficient sampling from a generic target probability density function (pdf). The new class of algorithms employs adaptive non-parametric proposal densities which become closer and closer to the target as the number of iterations increases. The proposal pdf is built using interpolation procedures based on a set of support points which is constructed iteratively based on previously drawn samples. The algorithm's efficiency is ensured by a test that controls the evolution of the set of support points. This extra stage controls the computational cost and the converge…
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…
The metropolis in retrospect : from the trading metropolis to the global metropolis.
2005
SummaryMetropolization is not a new phenomenon: metropolises have been around for centuries. The prime and permanent function of a metropolis is the coordination of economic activities at a world scale. This function has been applied to different activities in history, depending on technological conditions and economic organization, and consequently it generated different forms of metropolises. The resulting continuities and discontinuities in the metropolises' evolution can be understood in terms of agglomeration economies. In the pre-industrial period, the trading metropolis coordinates long range trade. The industrial revolutions generate new needs for coordination of production and give…
A la deriva entre cartells de cinema: una narrativa personal sobre París
2020
INTRODUCCIÓ. Les ciutats enquadrades des del cinema han construït una cultura visual amb la qual s’identifiquen els seus consumidors; imatges emblemàtiques que configuren una memòria col·lectiva. Però aquestes metròpolis només existeixen en la ficció. La realitat té poc o gens a veure amb aquestes visualitats. En aquesta investigació s’ofereix un relat alternatiu sobre la ciutat de París. MÈTODE. A partir d’una metodologia de recerca basada en l’art, s’empra la deriva com a mètode d’observació i anàlisi crítica de l’entorn urbà. L’objectiu és percebre París d’una manera alternativa a l’imaginari urbà creat pel cinema, a partir de la recerca de cartells de cinema que es troben en aquesta ciu…